Tag Archives: Data Analysis, Statistics and Probability

The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM [CEA]

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http://arxiv.org/abs/1905.01372


The interstellar medium (ISM) is a complex non-linear system governed by gravity and magneto-hydrodynamics, as well as radiative, thermodynamical, and chemical processes. Our understanding of it mostly progresses through observations and numerical simulations, and a quantitative comparison between these two approaches requires a generic and comprehensive statistical description. The goal of this paper is to build such a description, with the purpose to permit an efficient comparison independent of any specific prior or model. We start from the Wavelet Scattering Transform (WST), a low-variance statistical description of non-Gaussian processes, developed in data science, that encodes long-range interactions through a hierarchical multiscale approach based on the Wavelet transform. We perform a reduction of the WST through a fit of its angular dependencies, allowing to gather most of the information it contains into a few components whose physical meanings are identified, and that describe, e.g., isotropic and anisotropic behaviours. The result of this paper is the Reduced Wavelet Scattering Transform (RWST), a statistical description with a small number of coefficients that characterizes complex structures arising from non-linear phenomena, free from any specific prior. The RWST coefficients encode moments of order up to four, have reduced variances, and quantify the couplings between scales. To show the efficiency and generality of this description, we apply it successfully to three kinds of processes: fractional Brownian motions, MHD simulations, and Herschel observations in a molecular cloud. With fewer than 100 coefficients when probing 6 scales and 8 angles on 256*256 maps, we were able with the RWST to perform quantitative comparisons, to infer relevant physical properties, and to produce realistic synthetic fields.

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E. Allys, F. Levrier, S. Zhang, et. al.
Tue, 7 May 19
64/76

Comments: First version, submitted to A&A

STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources [HEAP]

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http://arxiv.org/abs/1905.00910


Gravitational-wave observations of double compact object (DCO) mergers are providing new insights into the physics of massive stars and the evolution of binary systems. Making the most of expected near-future observations for understanding stellar physics will rely on comparisons with binary population synthesis models. However, the vast majority of simulated binaries never produce DCOs, which makes calculating such populations computationally inefficient. We present an importance sampling algorithm, STROOPWAFEL, that improves the computational efficiency of population studies of rare events, by focusing the simulation around regions of the initial parameter space found to produce outputs of interest. We implement the algorithm in the binary population synthesis code COMPAS, and compare the efficiency of our implementation to the standard method of Monte Carlo sampling from the birth probability distributions. STROOPWAFEL finds $\sim$25-200 times more DCO mergers than the standard sampling method with the same simulation size, and so speeds up simulations by up to two orders of magnitude. Finding more DCO mergers automatically maps the parameter space with far higher resolution than when using the traditional sampling. This increase in efficiency also leads to a decrease of a factor $\sim$3-10 in statistical sampling uncertainty for the predictions from the simulations. This is particularly notable for the distribution functions of observable quantities such as the black hole and neutron star chirp mass distribution, including in the tails of the distribution functions where predictions using standard sampling can be dominated by sampling noise.

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F. Broekgaarden, S. Justham, S. Mink, et. al.
Mon, 6 May 19
41/58

Comments: Submitted. Data and the code for the STROOPWAFEL algorithm will be made publicly available after acceptance. Early inquiries can be addressed to the lead author

Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates [CL]

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http://arxiv.org/abs/1904.11006


We present an active-learning strategy for undergraduates that applies Bayesian analysis to candy-covered chocolate $\text{m&m’s}^\circledR$. The exercise is best suited for small class sizes and tutorial settings, after students have been introduced to the concepts of Bayesian statistics. The exercise takes advantage of the non-uniform distribution of $\text{m&m’s}^\circledR~$ colours, and the difference in distributions made at two different factories. In this paper, we provide the intended learning outcomes, lesson plan and step-by-step guide for instruction, and open-source teaching materials. We also suggest an extension to the exercise for the graduate-level, which incorporates hierarchical Bayesian analysis.

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G. Eadie, D. Huppenkothen, A. Springford, et. al.
Fri, 26 Apr 19
2/69

Comments: Accepted to the Journal of Statistics Education (in press); 15 pages, 7 figures

Why do some probabilistic forecasts lack reliability? [CL]

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http://arxiv.org/abs/1904.08791


In this work, we investigate the reliability of the probabilistic binary forecast. We mathematically prove that a necessary, but not sufficient, condition for achieving a reliable probabilistic forecast is maximizing the Peirce skill score (PSS) at the threshold probability of the climatological base rate. The condition is confirmed by using artificially synthesized forecast-outcome pair data and previously published probabilistic solar flare forecast models. The condition gives a partial answer as to why some probabilistic forecast system lack reliability, because the system, which does not satisfy the proved condition, can never be reliable. Therefore, the proved condition is very important for the developers of a probabilistic forecast system. The result implies that those who want to develop a reliable probabilistic forecast system must adjust or train the system so as to maximize PSS near the threshold probability of the climatological base rate.

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Y. Kubo
Mon, 22 Apr 19
2/36

Comments: 12 pages, 6 figures, 1 table, accepted for publication in the Journal of Space Weather and Space Climate (JSWSC)

A hierarchical test of general relativity with gravitational waves [CL]

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http://arxiv.org/abs/1904.08011


We propose a hierarchical approach to testing general relativity with multiple gravitational wave detections. Unlike existing strategies, our method does not assume that parameters quantifying deviations from general relativity are either common or completely unrelated accross all sources. We instead assume that these parameters follow some underlying distribution, which we parametrize and constrain. This can be then compared to the distribution expected from general relativity, i.e. no deviation in any of the events. We demonstrate that our method is robust to measurement uncertainties and can be applied to theories of gravity where the parameters beyond general relativity are related to each other, as generally expected. Our method contains the two extremes of common and unrelated parameters as limiting cases. We apply the hierarchical model to the population of 10 binary black hole systems so far detected by LIGO and Virgo. We do this for a parametrized test of gravitational wave generation, by assuming that the beyond-general-relativity parameters follow a Gaussian distribution. We compute the posterior distribution for the mean and the variance of the population and show that both are consistent with general relativity.

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M. Isi, K. Chatziioannou and W. Farr
Thu, 18 Apr 19
72/75

Comments: 5 pages, 3 figures

Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations [GA]

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http://arxiv.org/abs/1903.10457


We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations, EAGLE and Illustris. We train Convolutional Neural Networks to learn the relationship between synthetic galaxy spectra and high resolution SFHs. To evaluate our SFH reconstruction we use Symmetric Mean Absolute Percentage Error (SMAPE), which acts as a true percentage error in the low-error regime. On dust-attenuated spectra we achieve high test accuracy (median SMAPE = $12.0\%$). Including the effects of simulated experimental noise increases the error ($13.2\%$), however this is alleviated by including multiple realisations of the noise, which increases the training set size and reduces overfitting ($11.4\%$). We also make estimates for the experimental and modelling errors. To further evaluate the generalisation properties we apply models trained on one simulation to spectra from the other, which leads to only a small increase in the error ($\sim 16\%$). We apply each trained model to SDSS DR7 spectra, and find smoother histories than in the VESPA catalogue. This new approach complements the results of existing SED fitting techniques, providing star formation histories directly motivated by the results of the latest cosmological simulations.

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C. Lovell, V. Acquaviva, P. Thomas, et. al.
Tue, 26 Mar 19
49/72

Comments: 19 pages, 19 figures, submitted to Monthly Notices of the Royal Astronomical Society (MNRAS)

The Promise of Data Science for the Technosignatures Field [IMA]

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http://arxiv.org/abs/1903.08381


This paper outlines some of the possible advancements for the technosignatures searches using the new methods currently rapidly developing in computer science, such as machine learning and deep learning. It also showcases a couple of case studies of large research programs where such methods have been already successfully implemented with notable results. We consider that the availability of data from all sky, all the time observations paired with the latest developments in computational capabilities and algorithms currently used in artificial intelligence, including automation, will spur an unprecedented development of the technosignatures search efforts.

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A. Berea, S. Croft and D. Angerhausen
Thu, 21 Mar 19
29/66

Comments: Science white paper submitted in response to the the U.S. National Academies of Science, Engineering, and Medicine’s call for community input to the Astro2020 Decadal Survey; 7 pages, 1 figure

A Catalog of Redshift Estimates for 1366 BATSE Long-Duration Gamma-Ray Bursts: Evidence for Strong Selection Effects on the Phenomenological Prompt Gamma-Ray Correlations [HEAP]

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http://arxiv.org/abs/1903.06989


We present a catalog of the redshift estimates and probability distributions for 1366 individual Long-duration Gamma-Ray Bursts (LGRBs) detected by the Burst And Transient Source Experiment (BATSE). This result is based on a careful classification and modeling of the population distribution of BATSE LGRBs in the 5-dimensional space of redshift as well as intrinsic prompt gamma-ray emission properties: peak luminosity, total isotropic emission, the spectral peak energy, and the intrinsic duration, while taking into account the detection mechanism of BATSE and sample incompleteness. The underlying assumption in our modeling approach is that LGRBs trace the Cosmic Star Formation Rate and that the joint 4-dimensional distribution of the aforementioned prompt gamma-ray emission properties follows a multivariate log-normal distribution. Our modeling approach enables us to constrain the redshifts of BATSE LGRBs to average uncertainty ranges of $0.7$ and $1.7$ at $50\%$ and $90\%$ confidence levels, respectively. Our predictions are almost entirely at odds with the previous estimates of BATSE redshifts based on the phenomenological high-energy correlations, in particular with the estimates derived from the lag-luminosity and the variability-luminosity relations. There is, however, a weak but significant correlation of strength $\sim0.26$ between our predicted redshift estimates and those derived from the hardness-brightness relations. The discrepancies between the estimates can be explained by the strong influence of sample incompleteness in shaping the phenomenologically proposed high-energy correlations in the literature. The presented catalog here can be useful for demographic studies of LGRBs and studies of individual BATSE events.

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A. Shahmoradi and R. Nemiroff
Tue, 19 Mar 19
52/100

Comments: N/A

A method to deconvolve stellar rotational velocities III. The probability distribution function via Maximum Likelihood utilizing Finite Distribution Mixtures [SSA]

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http://arxiv.org/abs/1903.06623


The study of accurate methods to estimate the distribution of stellar rotational velocities is important for understanding many aspects of stellar evolution. From such observations we obtain the projected rotational speed v sin(i) in order to recover the true distribution of the rotational velocity. To that end, we need to solve a difficult inverse problem that can be posed as a Fredholm integral of the first kind. n this work we have used a novel approach based on Maximum likelihood (ML) estimation to obtain an approximation of the true rotational velocity probability density function expressed as a sum of known distribution families. In our proposal, the measurements have been treated as random variables drawn from the projected rotational velocity probability density function. We analyzed the case of Maxwellian sum approximation, where we estimated the parameters that define the sum of distributions. The performance of the proposed method is analyzed using Monte Carlo simulations considering two theoretical cases for the probability density function of the true rotational stellar velocities: i) an unimodal Maxwellian probability density distribution and ii) a bimodal Maxwellian probability density distribution. The results show that the proposed method yielded more accurate estimates in comparison with the Tikhonov regularization method, especially for small sample length N=50. Our proposal was evaluated using real data from three sets of measurements, and our findings were validated using three statistical tests. The ML approach with Maxwellian sum approximation is a accurate method to deconvolve the rotational velocity probability density function, even when the sample length is small (N= 50)

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R. Orellana, P. Escarate, M. Cure, et. al.
Mon, 18 Mar 19
89/102

Comments: N/A

Comment on "Optimal prior for Bayesian inference in a constrained parameter space" by S. Hannestad and T. Tram, arXiv:1710.08899 [CEA]

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http://arxiv.org/abs/1902.07667


The Jeffreys prior for a constrained part of a parameter space is the same as that for the unconstrained space, contrary to the assertions of Hannestad and Tram.

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R. Cousins
Thu, 21 Feb 19
47/54

Comments: N/A

Metric Gaussian Variational Inference [CL]

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http://arxiv.org/abs/1901.11033


A variational Gaussian approximation of the posterior distribution can be an excellent way to infer posterior quantities. However, to capture all posterior correlations the parametrization of the full covariance is required, which scales quadratic with the problem size. This scaling prohibits full-covariance approximations for large-scale problems. As a solution to this limitation we propose Metric Gaussian Variational Inference (MGVI). This procedure approximates the variational covariance such that it requires no parameters on its own and still provides reliable posterior correlations and uncertainties for all model parameters. We approximate the variational covariance with the inverse Fisher metric, a local estimate of the true posterior uncertainty. This covariance is only stored implicitly and all necessary quantities can be extracted from it by independent samples drawn from the approximating Gaussian. MGVI requires the minimization of a stochastic estimate of the Kullback-Leibler divergence only with respect to the mean of the variational Gaussian, a quantity that only scales linearly with the problem size. We motivate the choice of this covariance from an information geometric perspective. The method is validated against established approaches in a small example and the scaling is demonstrated in a problem with over a million parameters.

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J. Knollmüller and T. Enßlin
Fri, 1 Feb 19
45/61

Comments: NIFTy5 release paper, 30 pages, 15 figures, submitted to jmlr, code is part of NIFTy5 release at this https URL

Posterior inference unchained with EL_2O [CL]

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http://arxiv.org/abs/1901.04454


Statistical inference of analytically non-tractable posteriors is a difficult problem because of marginalization of correlated variables and stochastic methods such as MCMC and VI are commonly used. We argue that stochastic KL divergence minimization used by MCMC and VI is noisy, and we propose instead EL_2O, expectation optimization of L_2 distance squared between the approximate log posterior q and the un-normalized log posterior of p. When sampling from q the solutions agree with stochastic KL divergence minimization based VI in the large sample limit, however EL_2O method is free of sampling noise, has better optimization properties, and requires only as many sample evaluations as the number of parameters we are optimizing if q covers p. As a consequence, increasing the expressivity of q improves both the quality of results and the convergence rate, allowing EL_2O to approach exact inference. Use of automatic differentiation methods enables us to develop Hessian, gradient and gradient free versions of the method, which can determine M(M+2)/2+1, M+1 and 1 parameter(s) of q with a single sample, respectively. EL_2O provides a reliable estimate of the quality of the approximating posterior, and converges rapidly on full rank gaussian approximation for q and extensions beyond it, such as nonlinear transformations and gaussian mixtures. These can handle general posteriors, while still allowing fast analytic marginalizations. We test it on several examples, including a realistic 13 dimensional galaxy clustering analysis, showing that it is several orders of magnitude faster than MCMC, while giving smooth and accurate non-gaussian posteriors, often requiring a few to a few dozen of iterations only.

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U. Seljak and B. Yu
Fri, 18 Jan 19
43/55

Comments: 35 pages, 6 figures

A binned likelihood for stochastic models [CL]

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http://arxiv.org/abs/1901.04645


Metrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these questions often rely on a likelihood function, which describes the plausibility of model parameters given observed data. In some complex systems or experimental setups predicting the outcome of a model cannot be done analytically and Monte Carlo techniques are used. In this paper, we present a new analytic likelihood that takes into account Monte Carlo uncertainties, appropriate for use in large or small statistics regimes. Our formulation has better performance than semi-analytic methods, prevents strong claims on biased statements, and results in better coverage properties than available methods.

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C. Argüelles, A. Schneider and T. Yuan
Wed, 16 Jan 19
20/76

Comments: 13 pages, 4 figures, 1 table, code can be found at this https URL

Thermonuclear fusion rates for tritium + deuterium using Bayesian methods [CL]

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http://arxiv.org/abs/1901.04857


The $^3$H(d,n)$^4$He reaction has a large low-energy cross section and will likely be utilized in future commercial fusion reactors. This reaction also takes place during big bang nucleosynthesis. Studies of both scenarios require accurate and precise fusion rates. To this end, we implement a one-level, two-channel R-matrix approximation into a Bayesian model. Our main goals are to predict reliable astrophysical S-factors and to estimate R-matrix parameters using the Bayesian approach. All relevant parameters are sampled in our study, including the channel radii, boundary condition parameters, and data set normalization factors. In addition, we take uncertainties in both measured bombarding energies and S-factors rigorously into account. Thermonuclear rates and reactivities of the $^3$H(d,n)$^4$He reaction are derived by numerically integrating the Bayesian S-factor samples. The present reaction rate uncertainties at temperatures between $1.0$ MK and $1.0$ GK are in the range of 0.2% to 0.6%. Our reaction rates differ from previous results by 2.9% near 1.0 GK. Our reactivities are smaller than previous results, with a maximum deviation of 2.9% near a thermal energy of $4$ keV. The present rate or reactivity uncertainties are more reliable compared to previous studies that did not include the channel radii, boundary condition parameters, and data set normalization factors in the fitting. Finally, we investigate previous claims of electron screening effects in the published $^3$H(d,n)$^4$He data. No such effects are evident and only an upper limit for the electron screening potential can be obtained.

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R. Souza, S. Boston, A. Coc, et. al.
Wed, 16 Jan 19
65/76

Comments: Phys. Rev. C, 2019, in press

Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning [CL]

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http://arxiv.org/abs/1901.00869


Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor, particularly in realistic detection scenarios. In this article we demonstrate that deep learning can handle the non-Gaussian and non-stationary nature of gravitational wave data, and showcase its application to denoise the gravitational wave signals generated by the binary black hole mergers GW150914, GW170104, GW170608 and GW170814 from advanced LIGO noise. To exhibit the accuracy of this methodology, we compute the overlap between the time-series signals produced by our denoising algorithm, and the numerical relativity templates that are expected to describe these gravitational wave sources, finding overlaps greater than 0.99. We discuss the implications of these results for the characterization of gravitational wave signals.

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W. Wei and E. Huerta
Mon, 7 Jan 19
7/52

Comments: 8 pages, 5 figures

Observation data pre-processing and scientific data products generation of POLAR [IMA]

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http://arxiv.org/abs/1901.00800


POLAR is a compact space-borne detector initially designed to measure the polarization of hard X-ray emitted from Gamma-Ray Bursts in the energy range 50-500keV. This instrument has been launched successfully onboard the Chinese space laboratory TG-2 on 15th September, 2016. After being switched on a few days later, tens of gigabytes of detection raw data were produced in-orbit by POLAR and transferred to ground every day. Before the launch date, a full pipeline and the related software were designed and developed for the purpose to quickly pre-process all the raw data of POLAR, which include both science data and engineering data, then to generate the high level scientific data products that are suitable for later science analysis. This pipeline has been successfully applied for the use by the POLAR Science Data Center in IHEP after POLAR was launched and switched on. A detailed introduction of the pipeline and some of the core relevant algorithms are presented in this paper.

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Z. Li, J. Sun, L. Song, et. al.
Fri, 4 Jan 19
9/38

Comments: 15 pages, 5 figures, 3 tables; Preprint submitted to RAA

Median statistics estimate of the neutron lifetime [CL]

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http://arxiv.org/abs/1812.09671


We construct the error distributions for the neutron lifetime using a subset of measurements compiled in the 2018 edition of Particle Data Group (PDG), as well as a few recent measurements, which are not yet included in PDG. We then checked the Gaussianity of the error distribution (using the techniques pioneered by Ratra and collaborators). We find that the error distributions using the weighted mean as well as median estimate are not consistent with a Gaussian distribution. We find that the Student’s $t$ and Cauchy distribution provide a better fit to the residuals. We then argue that median statistics based estimate should be used for the central estimate of the neutron lifetime. This median statistic estimate of the neutron lifetime from these measurements is given by $881.5 \pm 0.47$ seconds. We also note that the discrepancy between beam and bottle-based measurements using median statistics based estimates of the neutron lifetime persists to between 4-8$\sigma$, depending on which combination of measurements are used.

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A. Rajan and S. Desai
Thu, 27 Dec 18
9/80

Comments: 5 pages

Median statistics estimate of the neutron lifetime [CL]

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http://arxiv.org/abs/1812.09671


We construct the error distributions for the neutron lifetime using a subset of measurements compiled in the 2018 edition of Particle Data Group (PDG), as well as a few recent measurements, which are not yet included in PDG. We then checked the Gaussianity of the error distribution (using the techniques pioneered by Ratra and collaborators). We find that the error distributions using the weighted mean as well as median estimate are not consistent with a Gaussian distribution. We find that the Student’s $t$ and Cauchy distribution provide a better fit to the residuals. We then argue that median statistics based estimate should be used for the central estimate of the neutron lifetime. This median statistic estimate of the neutron lifetime from these measurements is given by $881.5 \pm 0.47$ seconds. We also note that the discrepancy between beam and bottle-based measurements using median statistics based estimates of the neutron lifetime persists to between 4-8$\sigma$, depending on which combination of measurements are used.

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A. Rajan and S. Desai
Thu, 27 Dec 18
63/80

Comments: 5 pages

Bayesian parameter estimation of miss-specified models [CL]

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http://arxiv.org/abs/1812.08194


Fitting a simplifying model with several parameters to real data of complex objects is a highly nontrivial task, but enables the possibility to get insights into the objects physics. Here, we present a method to infer the parameters of the model, the model error as well as the statistics of the model error. This method relies on the usage of many data sets in a simultaneous analysis in order to overcome the problems caused by the degeneracy between model parameters and model error. Errors in the modeling of the measurement instrument can be absorbed in the model error allowing for applications with complex instruments.

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J. Oberpriller and T. Enßlin
Fri, 21 Dec 18
19/72

Comments: N/A

Finding the origin of noise transients in LIGO data with machine learning [CL]

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http://arxiv.org/abs/1812.05225


Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.

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M. Cavaglia, K. Staats and T. Gill
Fri, 14 Dec 18
45/58

Comments: 24 pages, 11 figures

Cosmology-marginalized approaches in Bayesian model comparison: the neutrino mass as a case study [CEA]

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http://arxiv.org/abs/1812.05449


We propose here a \emph{novel} method which singles out the \emph{a priori} unavoidable dependence on the underlying cosmological model when extracting parameter constraints, providing robust limits which only depend on the considered dataset. Interestingly, when dealing with several possible cosmologies and interpreting the Bayesian preference in terms of the Gaussian statistical evidence, the preferred model is much less favored than when only two cases are compared. As a working example, we apply our approach to the cosmological neutrino mass bounds, which play a fundamental role not only in establishing the contribution of relic neutrinos to the dark matter of the Universe, but also in the planning of future experimental searches of the neutrino character and of the neutrino mass ordering.

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S. Gariazzo and O. Mena
Fri, 14 Dec 18
57/58

Comments: 6 pages, 1 table, 2 figures

On Probability and Cosmology: Inference Beyond Data? [CL]

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http://arxiv.org/abs/1812.04149


Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge. A central issue is what defines a ‘good’ model. When addressing global properties of the Universe or its initial state this becomes a particularly pressing issue. How to assess the probability of the Universe as a whole is empirically ambiguous, since we can examine only part of a single realisation of the system under investigation: at some point, data will run out. We review the basics of applying Bayesian statistical explanation to the Universe as a whole. We argue that a conventional Bayesian approach to model inference generally fails in such circumstances, and cannot resolve, e.g., the so-called ‘measure problem’ in inflationary cosmology. Implicit and non-empirical valuations inevitably enter model assessment in these cases. This undermines the possibility to perform Bayesian model comparison. One must therefore either stay silent, or pursue a more general form of systematic and rational model assessment. We outline a generalised axiological Bayesian model inference framework, based on mathematical lattices. This extends inference based on empirical data (evidence) to additionally consider the properties of model structure (elegance) and model possibility space (beneficence). We propose this as a natural and theoretically well-motivated framework for introducing an explicit, rational approach to theoretical model prejudice and inference beyond data.

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M. Sahlén
Wed, 12 Dec 18
89/92

Comments: 23 pages, 2 figures. Slightly expanded version of contributed chapter. Selected in “The Best Writing on Mathematics 2018”, M. Pitici (Ed.), Princeton University Press 2019

Very High Energy Ground Based Gamma Ray Telescopy Using TACTIC [IMA]

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http://arxiv.org/abs/1812.03429


This project is a study of VHE gamma ray astronomy using atmospheric Cherenkov technique. The project involved the study of processes of interaction of gamma rays, formation of extensive air showers, imaging of the Cherenkov radiation and data analysis of the observed data of Crab Nebula and MRK421 using TACTIC at Mt. Abu, India.

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M. Reddy, A. Gupta, S. Padhy, et. al.
Tue, 11 Dec 18
17/77

Comments: N/A

Particle identification in ground-based gamma-ray astronomy using convolutional neural networks [IMA]

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http://arxiv.org/abs/1812.01551


Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.

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E. Postnikov, I. Bychkov, J. Dubenskaya, et. al.
Wed, 5 Dec 18
47/73

Comments: 5 pages, 2 figures. Submitted to CEUR Workshop Proceedings, 8th International Conference “Distributed Computing and Grid-technologies in Science and Education” GRID 2018, 10 – 14 September 2018, Dubna, Russia

Beyond single-threshold searches: the Event Stacking Test [CL]

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http://arxiv.org/abs/1811.01297


We present a new statistical test that examines the consistency of the tails of two empirical distributions at multiple thresholds. Such distributions are often encountered in counting experiments, in physics and elsewhere, where the significance of populations of events is evaluated. This multi-threshold approach has the effect of “stacking” multiple events into the tail bin of the distribution, and thus we call it the Event Stacking Test. This test has the ability to confidently detect inconsistencies composed of multiple events, even if these events are low-significance outliers in isolation. We derive the Event Stacking Test from first principles and show that the p-value it reports is a well-calibrated representation of noise fluctuations. When applying this test to the detection of gravitational-wave transients in LIGO-Virgo data, we find that it performs better than or comparably to other statistical tests historically used within the gravitational-wave community. This test is particularly well-suited for detecting classes of gravitational-wave transients that are minimally-modeled, i.e., gravitational-wave bursts. We show that the Event Stacking Test allows us to set upper limits on the astrophysical rate-density of gravitational-wave bursts that are stricter than those set using other statistical tests by factors of up to 2 – 3.

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R. Lynch, S. Vitale and E. Katsavounidis
Tue, 6 Nov 18
50/77

Comments: N/A

Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection [IMA]

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http://arxiv.org/abs/1810.09930


Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detect Dark Matter. Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presented. The algorithm uses the charge signal (S2) light distribution obtained by simulating events using a waveform generator. To deal with the computational effort required by the likelihood-free approach, we employ the Bayesian Optimization for Likelihood-Free Inference (BOLFI) algorithm. Together with BOLFI, prior distributions for the parameters of interest (x; y; e) and highly informative discrepancy measures to perform the analyses are introduced. We evaluate the quality of the proposed algorithm by a comparison against the currently existing alternative methods using a large-scale simulation study. BOLFI provides a natural probabilistic uncertainty measure for the reconstruction and it improved the accuracy of the reconstruction over the next best algorithm by up to 15% when focusing on events over a large radii (R > 30 cm). In addition, BOLFI provides the smallest uncertainties among all the tested methods.

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U. Simola, B. Pelssers, D. Barge, et. al.
Wed, 24 Oct 18
4/75

Comments: N/A

A method to search for long duration gravitational wave transients from isolated neutron stars using the generalized FrequencyHough [IMA]

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http://arxiv.org/abs/1810.09784


We describe a method to detect gravitational waves lasting $O(hours-days)$ emitted by young, isolated neutron stars, such as those that could form after a supernova or a binary neutron star merger, using advanced LIGO/Virgo data. The method is based on a generalization of the FrequencyHough (FH), a pipeline that performs hierarchical searches for continuous gravitational waves by mapping points in the time/frequency plane of the detector to lines in the frequency/spindown plane of the source. We show that signals whose spindowns are related to their frequencies by a power law can be transformed to coordinates where the behavior of these signals is always linear, and can therefore be searched for by the FH. We estimate the sensitivity of our search across different braking indices, and describe the portion of the parameter space we could explore in a search using varying fast Fourier Transform (FFT) lengths.

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A. Miller, P. Astone, S. D’Antonio, et. al.
Wed, 24 Oct 18
62/75

Comments: 15 figures

Generating Event Triggers Based on Hilbert-Huang Transform and Its Application to Gravitational-Wave Data [IMA]

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http://arxiv.org/abs/1810.07555


We present a new event trigger generator based on the Hilbert-Huang transform, named EtaGen ($\eta$Gen). It decomposes a time-series data into several adaptive modes without imposing a priori bases on the data. The adaptive modes are used to find transients (excesses) in the background noises. A clustering algorithm is used to gather excesses corresponding to a single event and to reconstruct its waveform. The performance of EtaGen is evaluated by how many injections in the LIGO simulated data are found. EtaGen is viable as an event trigger generator when compared directly with the performance of Omicron, which is currently the best event trigger generator used in the LIGO Scientific Collaboration and Virgo Collaboration.

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E. Son, W. Kim, Y. Kim, et. al.
Thu, 18 Oct 18
50/75

Comments: 14 pages, 7 figures

The stepping-stone sampling algorithm for calculating the evidence of gravitational wave models [CL]

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http://arxiv.org/abs/1810.04488


Bayesian statistical inference has become increasingly important for the analysis of observations from the Advanced LIGO and Advanced Virgo gravitational-wave detectors. To this end, iterative simulation techniques, in particular nested sampling and parallel tempering, have been implemented in the software library LALInference to sample from the posterior distribution of waveform parameters of compact binary coalescence events. Nested sampling was mainly developed to calculate the marginal likelihood of a model but can produce posterior samples as a by-product. Thermodynamic integration is employed to calculate the evidence using samples generated by parallel tempering but has been found to be computationally demanding. Here we propose the stepping-stone sampling algorithm, originally proposed by Xie et al. (2011) in phylogenetics and a special case of path sampling, as an alternative to thermodynamic integration. The stepping-stone sampling algorithm is also based on samples from the power posteriors of parallel tempering but has superior performance as fewer temperature steps and thus computational resources are needed to achieve the same accuracy. We demonstrate its performance and computational costs in comparison to thermodynamic integration and nested sampling in a simulation study and a case study of computing the marginal likelihood of a binary black hole signal model applied to simulated data from the Advanced LIGO and Advanced Virgo gravitational wave detectors. To deal with the inadequate methods currently employed to estimate the standard errors of evidence estimates based on power posterior techniques, we propose a novel block bootstrap approach and show its potential in our simulation study and LIGO application.

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P. Russel, R. Meyer, J. Veitch, et. al.
Thu, 11 Oct 18
71/72

Comments: 10 pages, 5 figures, 2 tables

The projected mass distribution and the transition to homogeneity [CEA]

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http://arxiv.org/abs/1810.03539


A statistical analysis of the angular projection of the large-scale stellar mass distribution, as obtained from the Sloan Digital Sky Survey (data release 7) with the stellar masses of galaxies, finds values of the clustering length $r_0$ and the power-law exponent $\gamma$ of the two-point correlation function that are larger than the standard values, on account of the presence of very massive galaxies. A multifractal cosmic-web model with a transition to homogeneity at about 10 Mpc/h is still good, but there is considerable uncertainty in both this scale and the correlation dimension.

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J. Gaite
Tue, 9 Oct 18
42/77

Comments: 15 pages, 9 figures

Microlensing Searches for Exoplanets [EPA]

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http://arxiv.org/abs/1810.02691


Gravitational microlensing finds planets through their gravitational influence on the light coming from a more distant background star. The presence of the planet is then inferred from the tell-tale brightness variations of the background star during the lensing event, even if no light is detectable from the planet or the host foreground star. This review covers fundamental theoretical concepts in microlensing, addresses how observations are performed in practice, the~challenges of obtaining accurate measurements, and explains how planets reveal themselves in the data. It~concludes with a presentation of the most important findings to-date, a description of the method’s strengths and weaknesses, and a discussion of the future prospects of microlensing.

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Y. Tsapras
Mon, 8 Oct 18
11/43

Comments: 35 pages,9 figures, invited review for Geosciences Special Issue “Detection and Characterization of Extrasolar Planets”

Bayesian approach to SETI [IMA]

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http://arxiv.org/abs/1810.01207


The search for technosignatures from hypothetical galactic civilizations is going through a new phase of intense activity. For the first time, a significant fraction of the vast search space is expected to be sampled in the foreseeable future, potentially bringing informative data about the abundance of detectable extraterrestrial civilizations, or the lack thereof. Starting from the current state of ignorance about the galactic population of non-natural electromagnetic signals, we formulate a Bayesian statistical model to infer the mean number of radio signals crossing Earth, assuming either non-detection or the detection of signals in future surveys of the Galaxy. Under fairly noninformative priors, we find that not detecting signals within about $1$ kly from Earth, while suggesting the lack of galactic emitters or at best the scarcity thereof, is nonetheless still consistent with a probability exceeding $10$ \% that typically over $\sim 100$ signals could be crossing Earth, with radiated power analogous to that of the Arecibo radar, but coming from farther in the Milky Way. The existence in the Galaxy of potentially detectable Arecibo-like emitters can be reasonably ruled out only if all-sky surveys detect no such signals up to a radius of about $40$ kly, an endeavor requiring detector sensitivities thousands times higher than those of current telescopes. Conversely, finding even one Arecibo-like signal within $\sim 1000$ light years, a possibility within reach of current detectors, implies almost certainly that typically more than $\sim 100$ signals of comparable radiated power cross the Earth, yet to be discovered.

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C. Grimaldi and G. Marcy
Wed, 3 Oct 18
55/64

Comments: Published in PNAS ahead of print October 1, 2018. Preprint has 13 pages, 7 figures + 7 pages of Supplementary Information with 5 figures

Constraining neutrino mass with tomographic weak lensing one-point probability distribution function and power spectrum [CEA]

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http://arxiv.org/abs/1809.10747


We study the constraints on neutrino mass sum (M_nu) from the one-point probability distribution function (PDF) and power spectrum of weak lensing measurements for an LSST-like survey, using the MassiveNuS simulations. The PDF provides access to non-Gaussian information beyond the power spectrum. It is particularly sensitive to nonlinear growth on small scales, where massive neutrinos also have the largest effect. We find that tomography helps improve the constraint on M_nu by 14% and 32% for the power spectrum and the PDF, respectively, compared to a single redshift bin. The PDF alone outperforms the power spectrum in constraining M_nu. When the two statistics are combined, the constraint is further tightened by 35%. We conclude that weak lensing PDF is complementary to the power spectrum and has the potential to become a powerful tool for constraining neutrino mass.

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J. Liu and M. Madhavacheril
Mon, 1 Oct 18
5/46

Comments: 8 pages, 4 figures, comments welcome!

Astrophysical S-factors, thermonuclear rates, and electron screening potential for the $^3$He(d,p)$^{4}$He Big Bang reaction via a hierarchical Bayesian model [IMA]

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http://arxiv.org/abs/1809.06966


We developed a hierarchical Bayesian framework to estimate S-factors and thermonuclear rates for the $^3$He(d,p)$^{4}$He reaction, which impacts the primordial abundances of $^3$He and $^7$Li. The available data are evaluated and all direct measurements are taken into account in our analysis for which we can estimate separate uncertainties for systematic and statistical effects. For the nuclear reaction model, we adopt a single-level, two-channel approximation of R-matrix theory, suitably modified to take the effects of electron screening at lower energies into account. Apart from the usual resonance parameters (resonance location and reduced widths for the incoming and outgoing reaction channel), we include for the first time the channel radii and boundary condition in the fitting process. Our new analysis of the $^3$He(d,p)$^{4}$He S-factor data results in improved estimates for the thermonuclear rates. This work represents the first nuclear rate evaluation using the R-matrix theory embedded into a hierarchical Bayesian framework, properly accounting for all known sources of uncertainty. Therefore, it provides a test bed for future studies of more complex reactions.

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R. Souza, C. Iliadis and A. Coc
Thu, 20 Sep 18
23/55

Comments: N/A

Retrieval analysis of 38 WFC3 transmission spectra and resolution of the normalisation degeneracy [EPA]

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http://arxiv.org/abs/1809.06894


A comprehensive analysis of 38 previously published Wide Field Camera 3 (WFC3) transmission spectra is performed using a hierarchy of nested-sampling retrievals: with versus without clouds, grey versus non-grey clouds, isothermal versus non-isothermal transit chords and with water, hydrogen cyanide and/or ammonia. We revisit the “normalisation degeneracy”: the relative abundances of molecules are degenerate at the order-of-magnitude level with the absolute normalisation of the transmission spectrum. Using a suite of mock retrievals, we demonstrate that the normalisation degeneracy may be partially broken using WFC3 data alone, even in the absence of optical/visible data and without appealing to the presence of patchy clouds, although lower limits to the mixing ratios may be prior-dominated depending on the measurement uncertainties. With James Webb Space Telescope-like spectral resolutions, the normalisation degeneracy may be completely broken from infrared spectra alone. We find no trend in the retrieved water abundances across nearly two orders of magnitude in exoplanet mass and a factor of 5 in retrieved temperature (about 500 to 2500 K). We further show that there is a general lack of strong Bayesian evidence to support interpretations of non-grey over grey clouds (only for WASP-69b and WASP-76b) and non-isothermal over isothermal atmospheres (no objects). 35 out of 38 WFC3 transmission spectra are well-fitted by an isothermal transit chord with grey clouds and water only, while 8 are adequately explained by flat lines. Generally, the cloud composition is unconstrained.

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C. Fisher and K. Heng
Thu, 20 Sep 18
55/55

Comments: Accepted by MNRAS. 33 pages, 29 figures, 3 tables

Pyaneti: a fast and powerful software suite for multi-planet radial velocity and transit fitting [EPA]

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http://arxiv.org/abs/1809.04609


Transiting exoplanet parameter estimation from time-series photometry and Doppler spectroscopy is fundamental to study planets’ internal structures and compositions. Here we present the code pyaneti, a powerful and user-friendly software suite to perform multi-planet radial velocity and transit data fitting. The code uses a Bayesian approach combined with an MCMC sampling to estimate the parameters of planetary systems. We combine the numerical efficiency of FORTRAN, the versatility of PYTHON, and the parallelization of OpenMP to make pyaneti a fast and easy to use code. The package is freely available at https://github.com/oscaribv/pyaneti.

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O. Barragán, D. Gandolfi and G. Antoniciello
Fri, 14 Sep 18
5/65

Comments: 15 pages, 6 figures, 2 tables. Accepted for publication in MNRAS

Non-parametric uncertainties in the dark matter velocity distribution [CL]

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http://arxiv.org/abs/1809.02323


We investigate the impact of uncertainty in the velocity distribution of dark matter on direct detection experiments. We construct an entropic prior with a hyperparameter $\beta$ that describes the strength of our belief in an isotropic Maxwell-Boltzmann velocity distribution. By varying $\beta$, we interpolate between a halo-independent and halo-dependent analysis. We present a novel approximation for the marginalisation of this entropic prior that is applicable to any counting experiment. With this formula, we investigate the impact of the uncertainty in limits from XENON1T. For dark matter masses greater than about 60 GeV, we find extremely mild sensitivity to the distribution. Below about 60 GeV, the limit weakens by less than an order of magnitude if we assume an isotropic distribution in the galactic frame. If we permit anisotropic distributions, the limit further weakens, but at most by about two orders of magnitude. Lastly, we check the impact of parametric uncertainties and discuss the possible inclusion and impact of our technique in global fits.

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A. Fowlie
Mon, 10 Sep 18
45/58

Comments: N/A

Extracting distribution parameters from multiple uncertain observations with selection biases [CL]

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http://arxiv.org/abs/1809.02063


We derive a Bayesian framework for incorporating selection effects into population analyses. We allow for both measurement uncertainty in individual measurements and, crucially, for selection biases on the population of measurements, and show how to extract the parameters of the underlying distribution based on a set of observations sampled from this distribution. We illustrate the performance of this framework with an example from gravitational-wave astrophysics, demonstrating that the mass ratio distribution of merging compact-object binaries can be extracted from Malmquist-biased observations with substantial measurement uncertainty.

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I. Mandel, W. Farr and J. Gair
Fri, 7 Sep 18
19/65

Comments: N/A

Towards online triggering for the radio detection of air showers using deep neural networks [IMA]

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http://arxiv.org/abs/1809.01934


The detection of air-shower events via radio signals requires to develop a trigger algorithm for a clean discrimination between signal and background events in order to reduce the data stream coming from false triggers. In this contribution we will describe an approach to trigger air-shower events on a single-antenna level as well as performing an online reconstruction of the shower parameters using neural networks.

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F. Führer, T. Charnock, A. Zilles, et. al.
Fri, 7 Sep 18
21/65

Comments: To be published in the proceedings of the ARENA2018 conference

Integration with an Adaptive Harmonic Mean Algorithm [CL]

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http://arxiv.org/abs/1808.08051


Efficiently estimating the integral of functions in high dimensional spaces is a non-trivial task when an analytical solution cannot be calculated. A oft-encountered example is in the calculation of the marginal likelihood, in a context where a sampling algorithm such as a Markov Chain Monte Carlo provides samples of the function. We present the Adaptive Harmonic Mean Integration (AHMI) algorithm. Given samples drawn according to a probability distribution proportional to the function, the algorithm will estimate the integral of the function and the uncertainty of the estimate by applying a harmonic mean estimator to adaptively chosen subvolumes of the parameter space. We describe the algorithm and it’s mathematical properties, and report the results using it on multiple test cases of up to 20 dimensions.

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A. Caldwell, R. Schick, O. Schulz, et. al.
Mon, 27 Aug 18
5/46

Comments: N/A

A novel approach to assess the impact of the Fano factor on the sensitivity of low-mass dark matter experiments [IMA]

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http://arxiv.org/abs/1808.06967


As first suggested by U. Fano in the 1940s, the statistical fluctuation of the number of pairs produced in an ionizing interaction is known to be sub-Poissonian. The dispersion is reduced by the so-called “Fano factor”, which empirically encapsulates the correlations in the process of ionization. In modelling the energy response of an ionization measurement device, the effect of the Fano factor is commonly folded into the overall energy resolution. While such an approximate treatment is appropriate when a significant number of ionization pairs are expected to be produced, the Fano factor needs to be accounted for directly at the level of pair creation when only a few are expected. To do so, one needs a discrete probability distribution of the number of pairs created $N$ with independent control of both the expectation $\mu$ and Fano factor $F$. Although no distribution $P(N|\mu,F)$ with this convenient form exists, we propose the use of the COM-Poisson distribution together with strategies for utilizing it to effectively fulfill this need. We then use this distribution to assess the impact that the Fano factor may have on the sensitivity of low-mass WIMP search experiments.

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D. Durnford, Q. Arnaud and G. Gerbier
Wed, 22 Aug 18
40/62

Comments: 9 pages, 6 figures

Analysis of luminosity measurements of the pre-white dwarf PG 1159-035 [IMA]

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http://arxiv.org/abs/1808.05132


The study of the luminosity measurements of the pre-white dwarf PG 1159-035 has established the properties of the rich power spectrum of the detected radiation and, derived thereof, the physical properties of this celestial body. Those of the measurements which are available online are analysed in this work from a different perspective. After the measurements were band-passed, they were split into two (almost equal) parts, one yielding the training (learning) set (i.e., the database of embedding vectors and associated predictions), the other the test set. The optimal embedding dimension $m_0=10$ was obtained using Cao’s method; this result was confirmed by an analysis of the correlation dimension. Subsequently, the extraction of the maximal Lyapunov exponent $\lambda$ was pursued for embedding dimensions $m$ between $3$ and $12$; results were obtained after removing the prominent undulations of the out-of-sample prediction-error arrays $S (k)$ by fitting a monotonic function to the data. The grand mean of the values, obtained for sufficient embedding dimensions ($10 \leq m \leq 12$), was: $\lambda = (9.2 \pm 1.0 ({\rm stat.}) \pm 2.7 ({\rm syst.})) \cdot 10^{-2}~\Delta \tau^{-1}$, where $\Delta \tau=10$ s is the sampling interval in the measurements. On the basis of this significantly non-zero result, it may be concluded that the physical processes, underlying the variation of the luminosity of PG 1159-035, are non-linear. The aforementioned result for $\lambda$ was obtained using the $L^\infty$-norm distance; a larger, yet not incompatible, result was extracted with the Euclidean ($L^2$-norm) distance.

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E. Matsinos
Thu, 16 Aug 18
32/46

Comments: 36 pages, 9 figures, 6 tables

Comment on "An excess of massive stars in the local 30 Doradus starburst" [SSA]

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http://arxiv.org/abs/1807.09772


Schneider et al. (Science, 2018) used an ad hoc statistical method in their calculation of the stellar initial mass function. Adopting an improved approach, we reanalyse their data and determine a power law exponent of $2.05_{-0.14}^{+0.13}$. Alternative assumptions regarding data set completeness and the star formation history model can shift the inferred exponent to $2.11_{-0.19}^{+0.17}$ and $2.15_{-0.13}^{+0.13}$, respectively.

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W. Farr and I. Mandel
Fri, 27 Jul 18
6/75

Comments: published as a technical comment in Science

HMCF – Hamiltonian Monte Carlo Sampling for Fields – A Python framework for HMC sampling with NIFTy [CL]

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http://arxiv.org/abs/1807.02709


HMCF “Hamiltonian Monte Carlo for Fields”, is a software add-on for the NIFTy “Numerical Information Field Theory” framework implementing Hamilton Monte Carlo (HMC) sampling in Python. HMCF as well as NIFTy are designed to address field in- ference problems especially in – but not limited to – astrophysics. They are optimized to deal with the typically high number of degrees of freedom as well as their correlation structure. HMCF adds an HMC sampler to NIFTy that automatically adjusts the many free pa- rameters steering the HMC sampling machinery such as integration step size and the mass matrix according to the requirements of field inference. Furthermore, different convergence measures are available to check whether the burn-in phase has finished. Multiprocessing in the sense of running individual Markov chains (MC) on several cores is possible as well. A primary application of HMCF is to provide samples from the full field posterior and to verify conveniently approximate algorithms implemented in NIFTy.

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C. Lienhard and T. Enßlin
Tue, 10 Jul 18
44/79

Comments: 33 pages, 4 figures, available at this https URL , see also arXiv:1708.01073

Mining Gravitational-wave Catalogs To Understand Binary Stellar Evolution: A New Hierarchical Bayesian Framework [HEAP]

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http://arxiv.org/abs/1806.08365


Catalogs of stellar-mass compact binary systems detected by ground-based gravitational-wave instruments (such as LIGO and Virgo) will offer insights into the demographics of progenitor systems and the physics guiding stellar evolution. Existing techniques approach this through phenomenological modeling, discrete model selection, or model mixtures. Instead, we explore a novel technique that mines gravitational-wave catalogs to directly infer posterior probability distributions of the hyper-parameters describing formation and evolutionary scenarios (e.g. progenitor metallicity, kick parameters, and common-envelope efficiency). We use a bank of compact-binary population synthesis simulations to train a Gaussian-process emulator that acts as a prior on observed parameter distributions (e.g. chirp mass, redshift, rate). This emulator slots into a hierarchical population inference framework to extract the underlying astrophysical origins of systems detected by LIGO and Virgo. Our method is fast, easily expanded with additional simulations, and can be adapted for training on arbitrary population synthesis codes, as well as different detectors like LISA.

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S. Taylor and D. Gerosa
Mon, 25 Jun 18
44/54

Comments: 18 pages, 13 figures, 1 table. Submitted to PRD

PyUnfold: A Python Package for Iterative Unfolding [CL]

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http://arxiv.org/abs/1806.03350


PyUnfold is a Python package for incorporating imperfections of the measurement process into a data analysis pipeline. In an ideal world, we would have access to the perfect detector: an apparatus that makes no error in measuring a desired quantity. However, in real life, detectors have finite resolutions, characteristic biases that cannot be eliminated, less than full detection efficiencies, and statistical and systematic uncertainties. By building a matrix that encodes a detector’s smearing of the desired true quantity into the measured observable(s), a deconvolution can be performed that provides an estimate of the true variable. This deconvolution process is known as unfolding. The unfolding method implemented in PyUnfold accomplishes this deconvolution via an iterative procedure, providing results based on physical expectations of the desired quantity. Furthermore, tedious book-keeping for both statistical and systematic errors produces precise final uncertainty estimates.

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J. Bourbeau and Z. Hampel-Arias
Tue, 12 Jun 18
24/79

Comments: 17 pages

Supervised Machine Learning for Analysing Spectra of Exoplanetary Atmospheres [EPA]

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http://arxiv.org/abs/1806.03944


The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find the best-fit model. Known as atmospheric retrieval, it is a technique that originates from the Earth and planetary sciences. Such methods are very time-consuming and by necessity there is a compromise between physical and chemical realism versus computational feasibility. Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods. Here, we report an adaptation of the random forest method of supervised machine learning, trained on a pre-computed grid of atmospheric models, which retrieves full posterior distributions of the abundances of molecules and the cloud opacity. The use of a pre-computed grid allows a large part of the computational burden to be shifted offline. We demonstrate our technique on a transmission spectrum of the hot gas-giant exoplanet WASP-12b using a five-parameter model (temperature, a constant cloud opacity and the volume mixing ratios or relative abundance by number of water, ammonia and hydrogen cyanide). We obtain results consistent with the standard nested-sampling retrieval method. Additionally, we can estimate the sensitivity of the measured spectrum to constraining the model parameters and we can quantify the information content of the spectrum. Our method can be straightforwardly applied using more sophisticated atmospheric models and also to interpreting an ensemble of spectra without having to retrain the random forest.

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P. Marquez-Neila, C. Fisher, R. Sznitman, et. al.
Tue, 12 Jun 18
75/79

Comments: 11 pages, 7 figures, 1 table

A spatial likelihood analysis for MAGIC telescope data [IMA]

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http://arxiv.org/abs/1806.03167


Context. The increase in sensitivity of Imaging Atmospheric Cherenkov Telescopes (IACTs) has lead to numerous detections of extended $\gamma$-ray sources at TeV energies, sometimes of sizes comparable to the instrument’s field of view (FoV). This creates a demand for advanced and flexible data analysis methods, able to extract source information by utilising the photon counts in the entire FoV.
Aims. We present a new software package, “SkyPrism”, aimed at performing 2D (3D if energy is considered) fits of IACT data, possibly containing multiple and extended sources, based on sky images binned in energy. Though the development of this package was focused on the analysis of data collected with the MAGIC telescopes, it can further be adapted to other instruments, such as the future Cherenkov Telescope Array (CTA).
Methods. We have developed a set of tools that, apart from sky images (count maps), compute the instrument response functions (IRFs) of MAGIC (effective exposure throughout the FoV, point spread function (PSF), energy resolution and background shape), based on the input data, Monte-Carlo simulations and the pointing track of the telescopes. With this information, the presented package can perform a simultaneous maximum likelihood fit of source models of arbitrary morphology to the sky images providing energy spectra, detection significances, and upper limits.
Results. We demonstrate that the SkyPrism tool accurately reconstructs the MAGIC PSF, on and off-axis performance as well as the underlying background. We further show that for a point source analysis with MAGIC’s default observational settings, SkyPrism gives results compatible with those of the standard tools while being more flexible and widely applicable.

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I. Vovk, M. Strzys and C. Fruck
Mon, 11 Jun 18
13/50

Comments: 13 pages, 10 figures

Acceleration of Non-Linear Minimisation with PyTorch [IMA]

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http://arxiv.org/abs/1805.07439


I show that a software framework intended primarily for training of neural networks, PyTorch, is easily applied to a general function minimisation problem in science. The qualities of PyTorch of ease-of-use and very high efficiency are found to be applicable in this domain and lead to two orders of magnitude improvement in time-to-solution with very small software engineering effort.

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B. Nikolic
Tue, 22 May 18
34/69

Comments: 5 pages, 2 figures, 2 program listings. Full source code available at: this https URL

Response of solar irradiance to sunspot area variations [SSA]

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http://arxiv.org/abs/1805.04350


One of the important open questions in solar irradiance studies is whether long-term variability (i.e. on timescales of years and beyond) can be reconstructed by means of models that describe short-term variability (i.e. days) using solar proxies as inputs. Preminger and Walton (2005, GRL, 32, 14109) showed that the relationship between spectral solar irradiance and proxies of magnetic-flux emergence, such as the daily sunspot area, can be described in the framework of linear system theory by means of the impulse response. We significantly refine that empirical model by removing spurious solar-rotational effects and by including an additional term that captures long-term variations. Our results show that long-term variability cannot be reconstructed from the short-term response of the spectral irradiance, which cautions the extension of solar proxy models to these timescales. In addition, we find that the solar response is nonlinear in such a way that cannot be corrected simply by applying a rescaling to sunspot area.

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T. Wit, G. Kopp, A. Shapiro, et. al.
Mon, 14 May 18
2/54

Comments: 18 pages. Updated version of an article published in The Astrophysical Journal

Dark Matter Model or Mass, but Not Both: Assessing Near-Future Direct Searches with Benchmark-free Forecasting [CL]

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http://arxiv.org/abs/1805.04117


Forecasting the signal discrimination power of dark matter (DM) searches is commonly limited to a set of arbitrary benchmark points. We introduce new methods for benchmark-free forecasting that instead allow an exhaustive exploration and visualization of the phenomenological distinctiveness of DM models, based on standard hypothesis testing. Using this method, we reassess the signal discrimination power of future liquid Xenon and Argon direct DM searches. We quantify the parameter regions where various non-relativistic effective operators, millicharged DM, and magnetic dipole DM can be discriminated, and where upper limits on the DM mass can be found. We find that including an Argon target substantially improves the prospects for reconstructing the DM properties. We also show that only in a small region with DM masses in the range 20-100 GeV and DM-nucleon cross sections a factor of a few below current bounds can near-future Xenon and Argon detectors discriminate both the DM-nucleon interaction and the DM mass simultaneously. In all other regions only one or the other can be obtained.

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T. Edwards, B. Kavanagh and C. Weniger
Mon, 14 May 18
53/54

Comments: 6 pages, 4 figures + appendix. Code for the calculations throughout the paper can be found at this https URL

Optimality of the Maximum Likelihood estimator in Astrometry [IMA]

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http://arxiv.org/abs/1805.03673


The problem of astrometry is revisited from the perspective of analyzing the attainability of well-known performance limits (the Cramer-Rao bound) for the estimation of the relative position of light-emitting (usually point-like) sources on a CCD-like detector using commonly adopted estimators such as the weighted least squares and the maximum likelihood. Novel technical results are presented to determine the performance of an estimator that corresponds to the solution of an optimization problem in the context of astrometry. Using these results we are able to place stringent bounds on the bias and the variance of the estimators in close form as a function of the data. We confirm these results through comparisons to numerical simulations under a broad range of realistic observing conditions. The maximum likelihood and the weighted least square estimators are analyzed. We confirm the sub-optimality of the weighted least squares scheme from medium to high signal-to-noise found in an earlier study for the (unweighted) least squares method. We find that the maximum likelihood estimator achieves optimal performance limits across a wide range of relevant observational conditions. Furthermore, from our results, we provide concrete insights for adopting an adaptive weighted least square estimator that can be regarded as a computationally efficient alternative to the optimal maximum likelihood solution. We provide, for the first time, close-form analytical expressions that bound the bias and the variance of the weighted least square and maximum likelihood implicit estimators for astrometry using a Poisson-driven detector. These expressions can be used to formally assess the precision attainable by these estimators in comparison with the minimum variance bound.

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S. Espinosa, J. Silva, R. Mendez, et. al.
Fri, 11 May 18
15/61

Comments: 24 pages, 7 figures, 2 tables, 3 appendices. Accepted by Astronomy & Astrophysics

Cosmic Microwave Background Constraints in Light of Priors Over Reionization Histories [IMA]

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http://arxiv.org/abs/1804.08476


Non-parametric reconstruction or marginalization over the history of reionization using cosmic microwave background data necessarily assumes a prior over possible histories. We show that different but reasonable choices of priors can shift current and future constraints on the reionization optical depth, $\tau$, or correlated parameters such as the neutrino mass sum, $\Sigma m_\nu$, at the level of 0.3-0.4$\sigma$, i.e., that this analysis is somewhat prior dependent. We point out some prior-related problems with the commonly used principal component reconstruction, concluding that the significance of some recent hints of early reionization in Planck 2015 data has been overestimated. We also present the first non-parametric reconstruction applied to newer Planck intermediate (2016) data and find that the hints of early reionization disappear entirely in this more precise dataset. These results limit possible explanations of the EDGES 21cm signal which would have also significantly reionized the universe at $z\,{>}\,15$. Our findings about the dependence on priors motivate the pursuit of improved data or searches for physical reionization models which can reduce the prior volume. The discussion here of priors is of general applicability to other non-parametric reconstructions, for example of the primordial power spectrum, of the recombination history, or of the expansion rate.

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M. Millea and F. Bouchet
Tue, 24 Apr 18
67/87

Comments: 13 pages, 7 figures, submitted to Astronomy & Astrophysics

Diagnostic Tests for Nested Sampling Calculations [CL]

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http://arxiv.org/abs/1804.06406


Nested sampling is an increasingly popular technique for Bayesian computation – in particular for multimodal, degenerate and high-dimensional problems. Without appropriate settings, however, nested sampling software may fail to explore such posteriors fully; for example producing correlated samples or missing significant modes. This paper introduces new diagnostic tests to assess the reliability of both parameter estimation and evidence calculations using nested sampling software, and demonstrates them empirically. We present two new diagnostic plots for nested sampling, and give practical advice for nested sampling software users. Our diagnostic tests and diagrams are implemented in nestcheck: a publicly available python package for analysing nested sampling calculations which is compatible with results from MultiNest and PolyChord.

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E. Higson, W. Handley, M. Hobson, et. al.
Thu, 19 Apr 18
39/47

Comments: 21 pages + appendix, 13 figures

Topological data analysis and diagnostics of compressible MHD turbulence [CL]

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http://arxiv.org/abs/1804.04688


The predictions of mean-field electrodynamics can now be probed using direct numerical simulations of random flows and magnetic fields. When modelling astrophysical MHD, it is important to verify that such simulations are in agreement with observations. One of the main challenges in this area is to identify robust \it{quantitative} measures to compare structures found in simulations with those inferred from astrophysical observations. A similar challenge is to compare quantitatively results from different simulations. Topological data analysis offers a range of techniques, including the Betti numbers and persistence diagrams, that can be used to facilitate such a comparison. After describing these tools, we first apply them to synthetic random fields and demonstrate that, when the data are standardized in a straightforward manner, some topological measures are insensitive to either large-scale trends or the resolution of the data. Focusing upon one particular astrophysical example, we apply topological data analysis to HI observations of the turbulent interstellar medium (ISM) in the Milky Way and to recent MHD simulations of the random, strongly compressible ISM. We stress that these topological techniques are generic and could be applied to any complex, multi-dimensional random field.

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I. Makarenko, P. Bushby, A. Fletcher, et. al.
Mon, 16 Apr 18
41/52

Comments: 22 pages, 15 figures

Arbitrary-order Hilbert spectral analysis and intermittency in solar wind density fluctuations [CL]

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http://arxiv.org/abs/1804.02169


The properties of inertial and kinetic range solar wind turbulence have been investigated with the arbitrary-order Hilbert spectral analysis method, applied to high-resolution density measurements. Due to the small sample size, and to the presence of strong non-stationary behavior and large-scale structures, the classical structure function analysis fails to detect power law behavior in the inertial range, and may underestimate the scaling exponents. However, the Hilbert spectral method provides an optimal estimation of the scaling exponents, which have been found to be close to those for velocity fluctuations in fully developed hydrodynamic turbulence. At smaller scales, below the proton gyroscale, the system loses its intermittent multiscaling properties, and converges to a monofractal process. The resulting scaling exponents, obtained at small scales, are in good agreement with those of classical fractional Brownian motion, indicating a long-term memory in the process, and the absence of correlations around the spectral break scale. These results provide important constraints on models of kinetic range turbulence in the solar wind.

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F. Carbone, L. Sorriso-Valvo, T. Alberti, et. al.
Mon, 9 Apr 18
36/55

Comments: N/A

PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light [GA]

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http://arxiv.org/abs/1803.08925


The study of unobscured active galactic nuclei (AGN) and quasars depends on the reliable decomposition of the light from the AGN point source and the extended host galaxy light. The problem is typically approached using parametric fitting routines using separate models for the host galaxy and the point spread function (PSF). We present a new approach using a Generative Adversarial Network (GAN) trained on galaxy images. We test the method using Sloan Digital Sky Survey (SDSS) r-band images with artificial AGN point sources added which are then removed using the GAN and with parametric methods using GALFIT. When the AGN point source PS is more than twice as bright as the host galaxy, we find that our method, PSFGAN, can recover PS and host galaxy magnitudes with smaller systematic error and a lower average scatter ($49\%$). PSFGAN is more tolerant to poor knowledge of the PSF than parametric methods. Our tests show that PSFGAN is robust against a broadening in the PSF width of $\pm 50\%$ if it is trained on multiple PSF’s. We demonstrate that while a matched training set does improve performance, we can still subtract point sources using a PSFGAN trained on non-astronomical images. While initial training is computationally expensive, evaluating PSFGAN on data is more than $40$ times faster than GALFIT fitting two components. Finally, PSFGAN it is more robust and easy to use than parametric methods as it requires no input parameters.

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D. Stark, B. Launet, K. Schawinski, et. al.
Wed, 28 Mar 18
11/148

Comments: 17 pages, 18 figures, accepted for publication in MNRAS

Enhancing confidence in the detection of gravitational waves from compact binaries via Bayesian model comparison [CL]

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http://arxiv.org/abs/1803.09783


We show that gravitational-wave signals from compact binary mergers may be better distinguished from instrumental noise transients using Bayesian model comparison, even when the signal power is below the usual threshold for detection. This method could reject the vast majority of noise transients, and therefore increase sensitivity to weak gravitational waves. We demonstrate this using simulated signals, as well as data for GW150914 and LVT151012. Finally, we explore ways of incorporating our method into existing Advanced LIGO and Virgo searches to make them significantly more powerful.

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M. Isi, R. Smith, S. Vitale, et. al.
Wed, 28 Mar 18
137/148

Comments: N/A

Fractal analysis of the large-scale stellar mass distribution in the Sloan Digital Sky Survey [CEA]

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http://arxiv.org/abs/1803.07419


A novel fractal analysis of the cosmic web structure is carried out, employing the Sloan Digital Sky Survey, data release 7. We consider the large-scale stellar mass distribution, unlike other analyses, and determine its multifractal geometry, which is compared with the geometry of the cosmic web generated by cosmological N-body simulations. We find a good concordance, the common features being: (i) a minimum singularity strength equal to one, which corresponds to the edge of diverging energy and differs from the adhesion model prediction; (ii) a “supercluster set” of relatively high dimension where the mass concentrates; and (iii) a non-lacunar structure, like the one generated by the adhesion model.

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J. Gaite
Wed, 21 Mar 2018
41/61

Comments: 18 pages, 7 figures

Variational Inference as an alternative to MCMC for parameter estimation and model selection [IMA]

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http://arxiv.org/abs/1803.06473


Many problems in Astrophysics involve using Bayesian Inference to deal with problems of parameter estimation and model selection. In this paper, we introduce Variational Inference to solve these problems and compare how the results hold up to Markov Chain Monte Carlo which is the most common method. Variational Inference converts the inference problem into an optimization problem by approximating the posterior from a known family of distributions and using Kullback-Leibler divergence to measure closeness. Variational Inference takes advantage of fast optimization techniques which make it ideal to deal with large datasets and also makes it trivial to parallelize. As a proof of principle, we apply Variational Inference for parameter estimation and model comparison to four different problems in astrophysics where MCMC techniques were previously used: measuring exoplanet orbital parameters from radial velocity data, tests of periodicities in measurements of $G$, significance of a turnover in the spectral lag data of GRB 160625B , and estimating the mass of a galaxy cluster using weak lensing. We find that Variational Inference is much faster than MCMC for these problems.

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A. Jain, P. Srijith and S. Desai
Tue, 20 Mar 2018
15/68

Comments: 12 pages, 3 figures

Computational Techniques for the Analysis of Small Signals in High-Statistics Neutrino Oscillation Experiments [CL]

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http://arxiv.org/abs/1803.05390


The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the very high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events to use with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of binned event distributions in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

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Gen2. Collaboration-IceCube, M. Aartsen, M. Ackermann, et. al.
Thu, 15 Mar 2018
21/53

Comments: N/A

Gas, Dust, Stars, Star Formation and their Evolution in M33 at Giant Molecular Cloud Scales [GA]

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http://arxiv.org/abs/1803.04733


We report on a multi parameter analysis of giant molecular clouds (GMCs) in the nearby spiral galaxy M33. A catalog of GMCs identifed in 12CO(J=3-2) was used to compile associated 12CO(J=1-0), dust, stellar mass and star formation rate. Each of the 58 GMCs are categorized by their evolutionary stage. Applying the principal component analysis on these parameters, we construct two principal components PC1 and PC2 which retain 75% of the information in the original dataset. PC1 is interpreted as expressing the total interstellar matter content, and PC2 as the total activity of star formation. Young (<10Myr) GMCs occupy a distinct region in the PC1-PC2 plane, with lower ISM content and star formation activity compared to intermediate age and older clouds. Comparison of average cloud properties in different evolutionary stages imply that GMCs may be heated or grow denser and more massive via aggregation of diffuse material in their first ~10 Myr. The PCA also objectively identified a set of tight relations between ISM and star formation. The ratio of the two CO lines is nearly constant, but weakly modulated by massive star formation. Dust is more strongly correlated with the star formation rate than the CO lines, supporting recent findings that dust may trace molecular gas better than CO. Stellar mass contributes weakly to the star formation rate, reminiscent of an extended form of the Schmidt Kennicutt relation with the molecular gas term substituted by dust.

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S. Komugi, R. Miura, N. Kuno et. al.
Wed, 14 Mar 2018
45/61

Comments: 9 pages, 3 figures, accepted for publication in PASJ

Particle Identification In Camera Image Sensors Using Computer Vision [IMA]

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http://arxiv.org/abs/1803.04493


We present a deep learning, computer vision algorithm constructed for the purposes of identifying and classifying charged particles in camera image sensors. We apply our algorithm to data collected by the Distributed Electronic Cosmic-ray Observatory (DECO), a global network of smartphones that monitors camera image sensors for the signatures of cosmic rays and other energetic particles, such as those produced by radioactive decays. The algorithm, whose core component is a convolutional neural network, achieves classification performance comparable to human quality across four distinct DECO event topologies. We apply our model to the entire DECO data set and determine a selection that achieves $\ge90\%$ purity for all event types. In particular, we estimate a purity of $98\%$ when applied to cosmic-ray muons. The automated classification is run on the public DECO data set in real time in order to provide classified particle interaction images to users of the app and other interested members of the public.

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M. Winter, J. Bourbeau, S. Bravo et. al.
Wed, 14 Mar 2018
58/61

Comments: 14 pages, 14 figures, 1 table

Testing One Hypothesis Multiple Times: The Multidimensional Case [CL]

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http://arxiv.org/abs/1803.03858


The identification of new rare signals in data, the detection of a sudden change in a trend, and the selection of competing models, are among the most challenging problems in statistical practice. These challenges can be tackled using a test of hypothesis where a nuisance parameter is present only under the alternative, and a computationally efficient solution can be obtained by the “Testing One Hypothesis Multiple times” (TOHM) method. In the one-dimensional setting, a fine discretization of the space of the non-identifiable parameter is specified, and a global p-value is obtained by approximating the distribution of the supremum of the resulting stochastic process. In this paper, we propose a computationally efficient inferential tool to perform TOHM in the multidimensional setting. Here, the approximations of interest typically involve the expected Euler Characteristics (EC) of the excursion set of the underlying random field. We introduce a simple algorithm to compute the EC in multiple dimensions and for arbitrary large significance levels. This leads to an highly generalizable computational tool to perform inference under non-standard regularity conditions.

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S. Algeri and D. van Dyk
Tue, 13 Mar 2018
36/61

Comments: N/A

Muon Hunter: a Zooniverse project [IMA]

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http://arxiv.org/abs/1802.08907


The large datasets and often low signal-to-noise inherent to the raw data of modern astroparticle experiments calls out for increasingly sophisticated event classification techniques. Machine learning algorithms, such as neural networks, have the potential to outperform traditional analysis methods, but come with the major challenge of identifying reliably classified training samples from real data. Citizen science represents an effective approach to sort through the large datasets efficiently and meet this challenge. Muon Hunter is a project hosted on the Zooniverse platform, wherein volunteers sort through pictures of data from the VERITAS cameras to identify muon ring images. Each image is classified multiple times to produce a “clean” dataset used to train and validate a convolutional neural network model both able to reject background events and identify suitable calibration data to monitor the telescope performance as a function of time.

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R. Bird, M. Daniel, H. Dickinson, et. al.
Tue, 27 Feb 18
35/85

Comments: Presented at TAUP 2017

Objective Bayesian analysis of neutrino masses and hierarchy [CEA]

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http://arxiv.org/abs/1802.09450


Given the precision of current neutrino data, priors still impact noticeably the constraints on neutrino masses and their hierarchy. To avoid our understanding of neutrinos being driven by prior assumptions, we construct a prior that is mathematically minimally informative. Using the constructed uninformative prior, we find that the normal hierarchy is favoured but with inconclusive posterior odds of 5.1:1. Better data is hence needed before the neutrino masses and their hierarchy can be well constrained. We find that the next decade of cosmological data should provide conclusive evidence if the normal hierarchy with negligible minimum mass is correct, and if the uncertainty in the sum of neutrino masses drops below 0.025 eV. On the other hand, if neutrinos obey the inverted hierarchy, achieving strong evidence will be difficult with the same uncertainties. Our uninformative prior was constructed from principles of the Objective Bayesian approach. The prior is called a reference prior and is minimally informative in the specific sense that the information gain after collection of data is maximised. The prior is computed for the combination of neutrino oscillation data and cosmological data and still applies if the data improve.

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A. Heavens and E. Sellentin
Tue, 27 Feb 18
76/85

Comments: 15 pages. For submission to JCAP

A 5D, polarised, Bethe-Heitler event generator for $γ\to e^+e^-$ conversion [CL]

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http://arxiv.org/abs/1802.08253


I describe a new version of the 5D, exact, polarised, Bethe-Heitler event generator of $\gamma$-ray conversions to $e^+e^-$, developed in the context of the HARPO project, that is able to simulate successive events with different photon energies and on different atomic targets without any substantial CPU overhead. I extend the verification range down to 1 keV above threshold and up to 1 EeV. This work could pave the way to the precise simulation of the high-performance $\gamma$-ray telescopes and polarimeters of the post-Fermi-LAT area.

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D. Bernard
Mon, 26 Feb 18
32/49

Comments: Submitted to Nucl. Instrum. Meth. A

The NWRA Classification Infrastructure: Description and Extension to the Discriminant Analysis Flare Forecasting System (DAFFS) [SSA]

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http://arxiv.org/abs/1802.06864


A classification infrastructure built upon Discriminant Analysis has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling “null” and “bad” data in multi-parameter analysis, application of non-parametric multi-dimensional Discriminant Analysis, an extension through Bayes’ theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of “Research to Operations” efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

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K. Leka, G. Barnes and E. Wagner
Wed, 21 Feb 18
10/58

Comments: J. Space Weather Space Climate: Accepted / in press; access supplementary materials through journal; some figures are less than full resolution for arXiv

Extreme Value Analysis of Solar Flare Events [CL]

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http://arxiv.org/abs/1802.06100


Space weather events such as solar flares can be harmful for life and infrastructure on earth or in near-earth orbit. In this paper we employ extreme value theory (EVT) to model extreme solar flare events; EVT offers the appropriate tools for the study and estimation of probabilities for extrapolation to ranges outside of those that have already been observed. In the past such phenomena have been modelled as following a power law which may gives poor estimates of such events due to overestimation. The data used in the study were X-ray fluxes from NOAA/GOES and the expected return levels for Carrington or Halloween like events were calculated with the outcome that the existing data predict similar events happening in 110 and 38 years respectively.

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T. Tsiftsi and V. Luz
Tue, 20 Feb 18
31/54

Comments: 17 pages, 5 figures

On the Practical Applications of Information Field Dynamics [CL]

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http://arxiv.org/abs/1802.06000


In this study we explore a new simulation scheme for partial differential equations known as Information Field Dynamics (IFD). Information field dynamics attempts to improve on existing simulation schemes by incorporating Bayesian field inference into the simulation scheme. The field inference is truly Bayesian and thus depends on a notion of prior belief. A number of results are presented, both theoretical and practical. Many small fixes and results on the general theory are presented, before exploring two general classes of simulation schemes that are possible in the IFD framework. For both, we present a set of theoretical results alongside the development of a prototype scheme. The first class of models corresponds roughly to traditional fixed-grid numerical PDE solvers. The prior Bayesian assumption in these models is that the fields are smooth, and their correlation structure does not vary between locations. For these reasons we call them translation-invariant schemes. We show the requirements for stability of these schemes, but most importantly we prove that these schemes indeed converge to the true behaviour of the field in the limit of high resolutions. Convergence had never been shown for any previous IFD scheme. We also find the error scaling of these codes and show that they implement something very analogous to a high-order finite-difference derivative approximation, which are the most elementary and well-studied numerical schemes. This is an important result, which proves the validity of the IFD approach. The second class of schemes, called the SPH-like schemes are similar to existing Smooth Particle Hydrodynamics codes, in which the simulation grid moves with the flow of the field being modelled.

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M. Dupont
Mon, 19 Feb 18
37/41

Comments: N/A

Two- and Multi-dimensional Curve Fitting using Bayesian Inference [CL]

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http://arxiv.org/abs/1802.05339


Fitting models to data using Bayesian inference is quite common, but when each point in parameter space gives a curve, fitting the curve to a data set requires new nuisance parameters, which specify the metric embedding the one-dimensional curve into the higher-dimensional space occupied by the data. A generic formalism for curve fitting in the context of Bayesian inference is developed which shows how the aforementioned metric arises. The result is a natural generalization of previous works, and is compared to oft-used frequentist approaches and similar Bayesian techniques.

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A. Steiner
Fri, 16 Feb 18
21/42

Comments: N/A

Analysis of attitude errors in GRACE range-rate residuals – a comparison between SCA1B and the reprocessed attitude fused product (SCA1B +ACC1B) [IMA]

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http://arxiv.org/abs/1802.02634


The precision of the attitude in the inter-satellite ranging missions like GRACE is one of the important requirement. It is required not only for the mission performance but also for the good quality of the gravity field models which are estimated from the inter-satellite ranging measurements. Here we present a comparative study of the analysis of two attitude datasets. One of them is the standardSCA1Brelease 2 datasets provided by JPL NASA and another is the reprocessed attitude computed atTU Graz by combining the angular accelerations and the standardSCA1Brelease2 datasets. Further, we also present the impact of the attitude datasets on the inter-satellite range measurements by analyzing their residuals. Our analysis reveals the significant improvement in the attitude due to the reprocessed product and reduced value of residuals computed from the reprocessed attitude.

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S. Goswami
Fri, 9 Feb 18
19/57

Comments: N/A

Pinpointing astrophysical bursts of low-energy neutrinos embedded into the noise [HEAP]

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http://arxiv.org/abs/1801.09062


We propose a novel method to increase the probability of identifying impulsive astrophysical bursts of low-energy neutrinos. The proposed approach exploits the temporal structure differences between astrophysical bursts and background fluctuations and it allows us to pinpoint weak signals otherwise unlikely to be detected. With respect to previous search strategies, this method strongly reduces the misidentification probability, e.g. for Super Kamiokande this reduction is a factor of $\sim 9$ within a distance of $D\sim 200$ kpc without decreasing the detection efficiency. In addition, we extend the proposed method to a network of different detectors and we show that the Kamland $\&$ LVD background reduction is improved by a factor $\sim 20$ up to an horizon of $D\sim75$ kpc.

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C. Casentini, G. Pagliaroli, C. Vigorito, et. al.
Tue, 30 Jan 18
32/70

Comments: N/A

Expected Precision of Europa Clipper Gravity Measurements [EPA]

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http://arxiv.org/abs/1801.08946


The primary gravity science objective of NASA’s Clipper mission to Europa is to confirm the presence or absence of a global subsurface ocean beneath Europa’s Icy crust. Gravity field measurements obtained with a radio science investigation can reveal much about Europa’s interior structure. Here, we conduct extensive simulations of the radio science measurements with the anticipated spacecraft trajectory and attitude (17F12v2) and assets on the spacecraft and the ground, including antenna orientations and beam patterns, transmitter characteristics, and receiver noise figures. In addition to two-way Doppler measurements, we also include radar altimeter crossover range measurements. We concentrate on +/-2 hour intervals centered on the closest approach of each of the 46 flybys. Our covariance analyses reveal the precision with which the tidal Love number k2, second-degree gravity coefficients C20 and C22, and higher-order gravity coefficients can be determined. The results depend on the Deep Space Network (DSN) assets that are deployed to track the spacecraft. We find that some DSN allocations are sufficient to conclusively confirm the presence or absence of a global ocean. Given adequate crossover range performance, it is also possible to evaluate whether the ice shell is hydrostatic.

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A. Verma and J. Margot
Tue, 30 Jan 18
55/70

Comments: N/A

Cosmic String Detection with Tree-Based Machine Learning [CEA]

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http://arxiv.org/abs/1801.04140


We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of processed CMB maps that boost the cosmic string detectability. Our proposed classifiers, after training, give results improved over or similar to the claimed detectability levels of the existing methods for string tension, $G\mu$. They can make $3\sigma$ detection of strings with $G\mu \gtrsim 2.1\times 10^{-10}$ for noise-free, $0.9’$-resolution CMB observations. The minimum detectable tension increases to $G\mu \gtrsim 3.0\times 10^{-8}$ for a more realistic, CMB S4-like (II) strategy, still a significant improvement over the previous results.

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A. Sadr, M. Farhang, S. Movahed, et. al.
Mon, 15 Jan 18
37/59

Comments: 7 pages, 3 figures, 2 tables, Comments are welcome

EXONEST: The Bayesian Exoplanetary Explorer [EPA]

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http://arxiv.org/abs/1712.08894


The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits plan\’etaires) exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.

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K. Knuth, B. Placek, D. Angerhausen, et. al.
Wed, 27 Dec 2017
5/56

Comments: 30 pages, 8 figures, 5 tables. Presented at the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017) in Jarinu/SP Brasil

A matched filter approach for blind joint detection of galaxy clusters in X-ray and SZ surveys [CEA]

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http://arxiv.org/abs/1712.06607


The combination of X-ray and SZ observations can potentially improve the cluster detection efficiency when compared to using only one of these probes, since both probe the same medium: the hot ionized gas of the intra-cluster medium. We present a method based on matched multifrequency filters (MMF) for detecting galaxy clusters from SZ and X-ray surveys. This method builds on a previously proposed joint X-ray-SZ extraction method (Tarr\’io et al. 2016) and allows to blindly detect clusters, that is finding new clusters without knowing their position, size or redshift, by searching on SZ and X-ray maps simultaneously. The proposed method is tested using data from the ROSAT all-sky survey and from the Planck survey. The evaluation is done by comparison with existing cluster catalogues in the area of the sky covered by the deep SPT survey. Thanks to the addition of the X-ray information, the joint detection method is able to achieve simultaneously better purity, better detection efficiency and better position accuracy than its predecessor Planck MMF, which is based on SZ maps only. For a purity of 85%, the X-ray-SZ method detects 141 confirmed clusters in the SPT region, whereas to detect the same number of confirmed clusters with Planck MMF, we would need to decrease its purity to 70%. We provide a catalogue of 225 sources selected by the proposed method in the SPT footprint, with masses ranging between 0.7 and 14.5 $\cdot 10^{14}$ Msun and redshifts between 0.01 and 1.2.

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P. Tarrio, J. Melin and M. Arnaud
Wed, 20 Dec 17
46/83

Comments: 18 pages (before appendices), 15 figures, 6 tables, 3 appendices. Submitted to A&A. Revised version after referee comments

Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification [IMA]

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http://arxiv.org/abs/1712.03970


We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by decision tree based methods, which enables the visualization of the classification categories. Secondly, we investigate how the Mutual Information based Transductive Feature Selection (MINT) algorithm can be used to perform feature pre-selection. If one would like to provide only a small number of input features to a machine learning classification algorithm, feature pre-selection provides a method to determine which of the many possible input properties should be selected. Third is the use of the tree-interpreter package to enable popular decision tree based ensemble methods to be opened, visualized, and understood. This is done by additional analysis of the tree based model, determining not only which features are important to the model, but how important a feature is for a particular classification given its value. Lastly, we use decision boundaries from the model to revise an already existing method of classification, essentially asking the tree based method where decision boundaries are best placed and defining a new classification method.
We showcase these techniques by applying them to the problem of star-galaxy separation using data from the Sloan Digital Sky Survey (hereafter SDSS). We use the output of MINT and the ensemble methods to demonstrate how more complex decision boundaries improve star-galaxy classification accuracy over the standard SDSS frames approach (reducing misclassifications by up to $\approx33\%$). We then show how tree-interpreter can be used to explore how relevant each photometric feature is when making a classification on an object by object basis.

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X. Morice-Atkinson, B. Hoyle and D. Bacon
Wed, 13 Dec 17
37/84

Comments: 12 pages, 8 figures, 8 tables

Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data [CL]

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http://arxiv.org/abs/1712.01293


The finite size of Monte Carlo samples carries intrinsic uncertainty that can lead to a substantial bias in parameter estimation if it is neglected and the sample size is small. We introduce a probabilistic treatment of this problem by replacing the usual likelihood functions with novel generalized probability distributions that incorporate the finite statistics via suitable marginalization. These new PDFs are analytic, and can be used to replace the Poisson, multinomial, and sample-based unbinned likelihoods, which covers many use cases in high-energy physics. In the limit of infinite statistics, they reduce to the respective standard probability distributions. In the general case of arbitrary Monte Carlo weights, the expressions involve the fourth Lauricella function $F_D$, for which we find a new representation as a contour integral that allows an exact and efficient calculation. The result also entails a new expression for the probability generating function of the Dirichlet-multinomial distribution with integer parameters. We demonstrate the bias reduction of our approach with a typical toy Monte Carlo problem, estimating the normalization of a peak in a falling energy spectrum, and compare the results with previously published methods from the literature.

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T. Glusenkamp
Wed, 6 Dec 17
41/71

Comments: 31 pages, 16 figures

Filtration of the gravitational frequency shift in the radio links communication with Earth's satellite [CL]

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http://arxiv.org/abs/1712.00759


At present the Radioastron (RA) Earth’s satellite having very elliptic orbit is used for probing of the gravitational red shift effect [1, 2]. Objective of this test consists in the enhancing accuracy of measurement to check the correspondence of value of the effect to Einsten’s theory at one order of value better then in was done in the GP-A experiment [3]. There are two H-masers in disposal, one at the board of satellite and other at the Land Tracking Station (LTS). One can compare its mutual time rate using communication radio links between RA and LTS. In contrast with the GP-A experiment there is a possibility of measurement repetition and accumulation of data in the process of RA orbital circulation. In principle it might be resulted in the increasing of the integral accuracy. In this paper we investigate the achievable accuracy in the frame of particular method of the red shift extraction associated with the techical specific of RA mission.

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A. Gusev and V. Rudenko
Tue, 5 Dec 17
75/96

Comments: 15 pages,3 figures

Significance of an excess in a counting experiment: assessing the impact of systematic uncertainties and the case with Gaussian background [CL]

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http://arxiv.org/abs/1712.00118


Several experiments in high-energy physics and astrophysics can be treated as on/off measurements, where an observation potentially containing a new source or effect (“on” measurement) is contrasted with a background-only observation free of the effect (“off” measurement). In counting experiments, the significance of the new source or effect can be estimated with a widely-used formula from [LiMa], which assumes that both measurements are Poisson random variables. In this paper we study three other cases: i) the ideal case where the background measurement has no uncertainty, which can be used to study the maximum sensitivity that an instrument can achieve, ii) the case where the background estimate $b$ in the off measurement has an additional systematic uncertainty, and iii) the case where $b$ is a Gaussian random variable instead of a Poisson random variable. The latter case applies when $b$ comes from a model fitted on archival or ancillary data, or from the interpolation of a function fitted on data surrounding the candidate new source/effect. Practitioners typically use in this case a formula which is only valid when $b$ is large and when its uncertainty is very small, while we derive a general formula that can be applied in all regimes. We also develop simple methods that can be used to assess how much an estimate of significance is sensitive to systematic uncertainties on the efficiency or on the background. Examples of applications include the detection of short Gamma-Ray Bursts and of new X-ray or $\gamma$-ray sources.

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G. Vianello
Mon, 4 Dec 17
26/72

Comments: submitted

Multilevel Bayesian Parameter Estimation in the Presence of Model Inadequacy and Data Uncertainty [CL]

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http://arxiv.org/abs/1711.10599


Model inadequacy and measurement uncertainty are two of the most confounding aspects of inference and prediction in quantitative sciences. The process of scientific inference (the inverse problem) and prediction (the forward problem) involve multiple steps of data analysis, hypothesis formation, model construction, parameter estimation, model validation, and finally, the prediction of the quantity of interest. This article seeks to clarify the concepts of model inadequacy and bias, measurement uncertainty, and the two traditional classes of uncertainty: aleatoric versus epistemic, as well as their relationships with each other in the process of scientific inference. Starting from basic principles of probability, we build and explain a hierarchical Bayesian framework to quantitatively deal with model inadequacy and noise in data. The methodology can be readily applied to many common inference and prediction problems in science, engineering, and statistics.

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A. Shahmoradi
Thu, 30 Nov 17
11/77

Comments: N/A

A recurrent neural network for classification of unevenly sampled variable stars [IMA]

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http://arxiv.org/abs/1711.10609


Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time (“light curves”). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to intranight cadence choices as well as diurnal and seasonal constraints. With nightly observations of millions of variable stars and transients from upcoming surveys, efficient and accurate discovery and classification techniques on noisy, irregularly sampled data must be employed with minimal human-in-the-loop involvement. Machine learning for inference tasks on such data traditionally requires the laborious hand-coding of domain-specific numerical summaries of raw data (“features”). Here we present a novel unsupervised autoencoding recurrent neural network (RNN) that makes explicit use of sampling times and known heteroskedastic noise properties. When trained on optical variable star catalogs, this network produces supervised classification models that rival other best-in-class approaches. We find that autoencoded features learned on one time-domain survey perform nearly as well when applied to another survey. These networks can continue to learn from new unlabeled observations and may be used in other unsupervised tasks such as forecasting and anomaly detection.

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B. Naul, J. Bloom, F. Perez, et. al.
Thu, 30 Nov 17
40/77

Comments: 23 pages, 14 figures. The published version is at Nature Astronomy (this https URL). Source code for models, experiments, and figures at this https URL (Zenodo Code DOI: 10.5281/zenodo.1045560)

Accuracy of inference on the physics of binary evolution from gravitational-wave observations [HEAP]

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http://arxiv.org/abs/1711.06287


The properties of the population of merging binary black holes encode some of the uncertain physics of the evolution of massive stars in binaries. The binary black hole merger rate and chirp mass distribution are being measured by ground-based gravitational-wave detectors. We consider isolated binary evolution and explore how accurately the physical model can be constrained with such observations by applying the Fisher information matrix to the merging black hole population simulated with the rapid binary population synthesis code COMPAS. We investigate variations in four COMPAS parameters: common envelope efficiency, kick velocity dispersion, and mass loss rates during the luminous blue variable and Wolf–Rayet stellar evolutionary phases. We find that 1000 observations would constrain these model parameters to a fractional accuracy of a few percent. Given the empirically determined binary black hole merger rate, we can expect gravitational-wave observations alone to place strong constraints on the physics of stellar and binary evolution within a few years.

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J. Barrett, S. Gaebel, C. Neijssel, et. al.
Mon, 20 Nov 17
23/56

Comments: N/A

Analysing Meteoroid Flights Using Particle Filters [EPA]

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http://arxiv.org/abs/1711.01726


Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a novel approach to modelling the uncertainty in meteoroid trajectories and incorporates errors in initial parameters, the dynamical model used and observed position measurements. Unlike other stochastic approaches, a particle filter does not require predefined values for initial conditions or unobservable trajectory parameters. The Bunburra Rockhole fireball (Spurn\’y et al. 2012), observed by the Australian Desert Fireball Network (DFN) in 2007, is used to determine the effectiveness of a particle filter for use in fireball trajectory modelling. The final mass is determined to be $2.16\pm1.33\, kg$ with a final velocity of $6030\pm216\, m\,s^{-1}$, similar to previously calculated values. The full automatability of this approach will allow an unbiased evaluation of all events observed by the DFN and lead to a better understanding of the dynamical state and size frequency distribution of asteroid and cometary debris in the inner solar system.

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E. Sansom, M. Rutten and P. Bland
Tue, 7 Nov 17
71/118

Comments: 12 pages, 2 figures, 3 tables

Calorimeter-less gamma-ray telescopes: Optimal measurement of charged particle momentum from multiple scattering by Bayesian analysis of Kalman filtering innovations [IMA]

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http://arxiv.org/abs/1710.10886


Novel gamma-ray telescope schemes (silicon wafer stacks, emulsions, gas detectors) are being developed so as to bridge the sensitivity gap between Compton and pair-creation telescopes. The lower average density with respect to the tungsten/silicon active target of the Fermi-LAT makes large effective-area telescopes voluminous objects, for which the photon energy measurement by conventional means (calorimeter, magnetic spectrometer, transition radiation detector) is a challenge for the mass budget of the space mission. We present an optimal measurement of track momentum by the multiple measurement of the angular deflections induced by multiple scattering in the active target itself, using a Bayesian analysis of the filtering innovations of a series of Kalman filters applied to the track. For a silicon-wafer-stack telescope, the method yields meaningful results up to a couple of GeV/c.

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D. Bernard and M. Frosini
Tue, 31 Oct 17
33/90

Comments: Presented at the 7th Fermi Symposium 2017, 15-20 October 2017, Garmisch-Partenkirchen, Germany. Submitted to Proceedings of Science (PoS(IFS2017)126)

Exoplanet Atmosphere Retrieval using Multifractal Analysis of Reflectance Spectra [EPA]

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http://arxiv.org/abs/1710.09870


We extend a data-based model-free multifractal method of exoplanet detection to probe exoplanetary atmospheres. Whereas the transmission spectrum is studied during the primary eclipse, we analyze what we call the reflectance spectrum, which is taken during the secondary eclipse phase, allowing a probe of the atmospheric limb. In addition to the spectral structure of exoplanet atmospheres, the approach provides information on phenomena such as hydrodynamical flows, tidal-locking behavior, and the dayside-nightside redistribution of energy. The approach is demonstrated using Spitzer data for exoplanet HD189733b. The central advantage of the method is the lack of model assumptions in the detection and observational schemes.

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S. Agarwal and J. Wettlaufer
Mon, 30 Oct 17
52/59

Comments: N/A

Halo-independence with quantified maximum entropy at DAMA/LIBRA [CL]

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http://arxiv.org/abs/1708.00181


Using the DAMA/LIBRA anomaly as an example, we formalise the notion of halo-independence in the context of Bayesian statistics and quantified maximum entropy. We consider an infinite set of possible profiles, weighted by an entropic prior and constrained by a likelihood describing noisy measurements of modulated moments by DAMA/LIBRA. Assuming an isotropic dark matter (DM) profile in the galactic rest frame, we find the most plausible DM profiles and predictions for unmodulated signal rates at DAMA/LIBRA. The entropic prior contains an a priori unknown regularisation factor, $\beta$, that describes the strength of our conviction that the profile is approximately Maxwellian. By varying $\beta$, we smoothly interpolate between a halo-independent and a halo-dependent analysis, thus exploring the impact of prior information about the DM profile.

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A. Fowlie
Fri, 20 Oct 17
33/42

Comments: 16 pages, 6 figures. Matches published version. Comments about isotropy added

Accretion-induced spin-wandering effects on the neutron star in Scorpius X-1: Implications for continuous gravitational wave searches [CL]

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http://arxiv.org/abs/1710.06185


The LIGO’s discovery of binary black hole mergers has opened up a new era of transient gravitational wave astronomy. The potential detection of gravitational radiation from another class of astronomical objects, rapidly spinning non-axisymmetric neutron stars, would constitute a new area of gravitational wave astronomy. Scorpius X-1 (Sco X-1) is one of the most promising sources of continuous gravitational radiation to be detected with present-generation ground-based gravitational wave detectors, such as Advanced LIGO and Advanced Virgo. As the sensitivity of these detectors improve in the coming years, so will power of the search algorithms being used to find gravitational wave signals. Those searches will still require integation over nearly year long observational spans to detect the incredibly weak signals from rotating neutron stars. For low mass X-ray binaries such as Sco X-1 this difficult task is compounded by neutron star “spin wandering” caused by stochastic accretion fluctuations. In this paper, we analyze X-ray data from the RXTE satellite to infer the fluctuating torque on the neutron star in Sco X-1. We then perform a large-scale simulation to quantify the statistical properties of spin-wandering effects on the gravitational wave signal frequency and phase evolution. We find that there are a broad range of expected maximum levels of frequency wandering corresponding to maximum drifts of between 0.3-50 {\mu}Hz/sec over a year at 99% confidence. These results can be cast in terms of the maximum allowed length of a coherent signal model neglecting spin-wandering effects as ranging between 5-80 days. This study is designed to guide the development and evaluation of Sco X-1 search algorithms.

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A. Mukherjee, C. Messenger and K. Riles
Wed, 18 Oct 2017
5/62

Comments: 13 pages, 12 figures

Data analysis recipes: Using Markov Chain Monte Carlo [IMA]

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http://arxiv.org/abs/1710.06068


Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data. In this primarily pedagogical contribution, we give a brief overview of the most basic MCMC method and some practical advice for the use of MCMC in real inference problems. We give advice on method choice, tuning for performance, methods for initialization, tests of convergence, troubleshooting, and use of the chain output to produce or report parameter estimates with associated uncertainties. We argue that autocorrelation time is the most important test for convergence, as it directly connects to the uncertainty on the sampling estimate of any quantity of interest. We emphasize that sampling is a method for doing integrals; this guides our thinking about how MCMC output is best used.

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D. Hogg and D. Foreman-Mackey
Wed, 18 Oct 2017
27/62

Comments: A purely pedagogical contribution

Non-Gaussian Error Distributions of Galactic Rotation Speed Measurements [IMA]

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http://arxiv.org/abs/1710.06624


We construct the error distribution of galactic rotation curve ($\Theta$) measurements using 134 data points from the 162 measurements compiled in De Grijs et al. (arXiv:1709.02501), following the same procedures used in previous works by Ratra and collaborators. We determine the weighted mean of these measurements to be $\Theta_{Mean} = 226.73 \pm 0.70$ km/sec and the median estimate is calculated to be $\Theta_{Med} = 234.66\pm 0.58$ km/sec. We also checked if the error distribution (constructed using both the weighted mean and median as the estimate) shows a Gaussian distribution. We find using both the estimates that it has much wider tails than a Gaussian distribution. We also tried to fit the data to four distributions: Gaussian, Cauchy, double-exponential, and Students-t. The best fit is obtained using the Students-$t$ distribution for $n=2$ using the median value as the central estimate, corresponding to $p$-value of 0.19. All other distributions provide poorer fits to the data.

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A. Rajan and S. Desai
Thu, 19 Oct 17
45/61

Comments: N/A

Multi-Scale Pipeline for the Search of String-Induced CMB Anisotropies [CEA]

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http://arxiv.org/abs/1710.00173


We propose a multi-scale edge-detection algorithm to search for the Gott-Kaiser-Stebbins imprints of a cosmic string (CS) network on the Cosmic Microwave Background (CMB) anisotropies. Curvelet decomposition and extended Canny algorithm are used to enhance the string detectability. Various statistical tools are then applied to quantify the deviation of CMB maps having a cosmic string contribution with respect to pure Gaussian anisotropies of inflationary origin. These statistical measures include the one-point probability density function, the weighted two-point correlation function (TPCF) of the anisotropies, the unweighted TPCF of the peaks and of the up-crossing map, as well as their cross-correlation. We use this algorithm on a hundred of simulated Nambu-Goto CMB flat sky maps, covering approximately $10\%$ of the sky, and for different string tensions $G\mu$. On noiseless sky maps with an angular resolution of $0.9’$, we show that our pipeline detects CSs with $G\mu$ as low as $G\mu\gtrsim 4.3\times 10^{-10}$. At the same resolution, but with a noise level typical to a CMB-S4 phase II experiment, the detection threshold would be to $G\mu\gtrsim 1.2 \times 10^{-7}$.

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A. Sadr, S. Movahed, M. Farhang, et. al.
Tue, 3 Oct 2017
27/63

Comments: 13 pages, 5 figures, 1 table, Comments are welcome

Orbits for eighteen visual binaries and two double-line spectroscopic binaries observed with HRCAM on the CTIO SOAR 4m telescope, using a new Bayesian orbit code based on Markov Chain Monte Carlo [SSA]

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http://arxiv.org/abs/1709.06582


We present orbital elements and mass sums for eighteen visual binary stars of spectral types B to K (five of which are new orbits) with periods ranging from 20 to more than 500 yr. For two double-line spectroscopic binaries with no previous orbits, the individual component masses, using combined astrometric and radial velocity data, have a formal uncertainty of ~0.1 MSun. Adopting published photometry, and trigonometric parallaxes, plus our own measurements, we place these objects on an H-R diagram, and discuss their evolutionary status. These objects are part of a survey to characterize the binary population of stars in the Southern Hemisphere, using the SOAR 4m telescope+HRCAM at CTIO. Orbital elements are computed using a newly developed Markov Chain Monte Carlo algorithm that delivers maximum likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate the uncertainty of our derived parameters in a robust way. For spectroscopic binaries, using our approach, it is possible to derive a self-consistent parallax for the system from the combined astrometric plus radial velocity data (“orbital parallax”), which compares well with the trigonometric parallaxes. We also present a mathematical formalism that allows a dimensionality reduction of the feature space from seven to three search parameters (or from ten to seven dimensions – including parallax – in the case of spectroscopic binaries with astrometric data), which makes it possible to explore a smaller number of parameters in each case, improving the computational efficiency of our Markov Chain Monte Carlo code.

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R. Mendez, R. Claveria, M. Orchard, et. al.
Thu, 21 Sep 17
49/50

Comments: 32 pages, 9 figures, 6 tables. Detailed Appendix with methodology. Accepted by The Astronomical Journal

Field dynamics inference via spectral density estimation [CL]

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http://arxiv.org/abs/1708.05250


Stochastic differential equations (SDEs) are of utmost importance in various scientific and industrial areas. They are the natural description of dynamical processes whose precise equations of motion are either not known or too expensive to solve, e.g., when modeling Brownian motion. In some cases, the equations governing the dynamics of a physical system on macroscopic scales occur to be unknown since they typically cannot be deduced from general principles. In this work, we describe how the underlying laws of a stochastic process can be approximated by the spectral density of the corresponding process. Furthermore, we show how the density can be inferred from possibly very noisy and incomplete measurements of the dynamical field. Generally, inverse problems like these can be tackled with the help of Information Field Theory (IFT). For now, we restrict to linear and autonomous processes. Though, this is a non-conceptual limitation that may be omitted in future work. To demonstrate its applicability we employ our reconstruction algorithm on a time-series and spatio-temporal processes.

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P. Frank, T. Steininger and T. Ensslin
Fri, 18 Aug 17
47/47

Comments: 12 pages, 9 figures

Verification of operational solar flare forecast: Case of Regional Warning Center Japan [SSA]

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http://arxiv.org/abs/1707.07903


In this article, we discuss a verification study of an operational solar flare forecast in the Regional Warning Center (RWC) Japan. The RWC Japan has been issuing four-categorical deterministic solar flare forecasts for a long time. In this forecast verification study, we used solar flare forecast data accumulated over 16 years (from 2000 to 2015). We compiled the forecast data together with solar flare data obtained with the Geostationary Operational Environmental Satellites (GOES). Using the compiled data sets, we estimated some conventional scalar verification measures with 95% confidence intervals. We also estimated a multi-categorical scalar verification measure. These scalar verification measures were compared with those obtained by the persistence method and recurrence method. As solar activity varied during the 16 years, we also applied verification analyses to four subsets of forecast-observation pair data with different solar activity levels. We cannot conclude definitely that there are significant performance difference between the forecasts of RWC Japan and the persistence method, although a slightly significant difference is found for some event definitions. We propose to use a scalar verification measure to assess the judgment skill of the operational solar flare forecast. Finally, we propose a verification strategy for deterministic operational solar flare forecasting.

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Y. Kubo, M. Den and M. Ishii
Wed, 26 Jul 17
11/68

Comments: 29 pages, 7 figures and 6 tables. Accepted for publication in Journal of Space Weather and Space Climate (SWSC)

Big Data vs. complex physical models: a scalable inference algorithm [CL]

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http://arxiv.org/abs/1707.04476


The data torrent unleashed by current and upcoming instruments requires scalable analysis methods. Machine Learning approaches scale well. However, separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is usually an after-thought. Classic forward-folding analyses with Markov Chain Monte Carlo or Nested Sampling enable parameter estimation and model comparison, even for complex and slow-to-evaluate physical models. However, these approaches require independent runs for each data set, implying an unfeasible number of model evaluations in the Big Data regime. Here we present a new algorithm based on nested sampling, deriving parameter probability distributions for each observation. Importantly, in our method the number of physical model evaluations scales sub-linearly with the number of data sets, and we make no assumptions about homogeneous errors, Gaussianity, the form of the model or heterogeneity/completeness of the observations. Our method has immediate application in speeding up analyses of large surveys, integral-field-unit observations, and Monte Carlo simulations.

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J. Buchner
Mon, 17 Jul 17
44/45

Comments: Submitted to MNRAS. Comments welcome. Figure 6 demonstrates the scaling. Implementation at this https URL

Computing Entropies With Nested Sampling [CL]

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http://arxiv.org/abs/1707.03543


The Shannon entropy, and related quantities such as mutual information, can be used to quantify uncertainty and relevance. However, in practice, it can be difficult to compute these quantities for arbitrary probability distributions, particularly if the probability mass functions or densities cannot be evaluated. This paper introduces a computational approach, based on Nested Sampling, to evaluate entropies of probability distributions that can only be sampled. I demonstrate the method on three examples: a simple gaussian example where the key quantities are available analytically; (ii) an experimental design example about scheduling observations in order to measure the period of an oscillating signal; and (iii) predicting the future from the past in a heavy-tailed scenario.

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B. Brewer
Thu, 13 Jul 17
43/60

Comments: Submitted to Entropy. 18 pages, 3 figures. Software available at this https URL

Radio-flaring Ultracool Dwarf Population Synthesis [SSA]

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http://arxiv.org/abs/1707.02212


Over a dozen ultracool dwarfs (UCDs), low-mass objects of spectral types $\geq$M7, are known to be sources of radio flares. These typically several-minutes-long radio bursts can be up to 100\% circularly polarized and have high brightness temperatures, consistent with coherent emission via the electron cyclotron maser operating in $\sim$kG magnetic fields. Recently, the statistical properties of the bulk physical parameters that describe these UCDs have become adequately described to permit synthesis of the population of radio-flaring objects. For the first time, I construct a Monte Carlo simulator to model the population of these radio-flaring UCDs. This simulator is powered by Intel Secure Key (ISK)- a new processor technology that uses a local entropy source to improve random number generation that has heretofore been used to improve cryptography. The results from this simulator indicate that only $\sim$5% of radio-flaring UCDs within the local interstellar neighborhood ($<$25 pc away) have been discovered. I discuss a number of scenarios which may explain this radio-flaring fraction, and suggest that the observed behavior is likely a result of several factors. The performance of ISK as compared to other pseudorandom number generators is also evaluated, and its potential utility for other astrophysical codes briefly described.

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M. Route
Mon, 10 Jul 17
18/64

Comments: Accepted for publication in ApJ; 18 pages, 4 figures

Charged particle tracking without magnetic field: optimal measurement of track momentum by a Bayesian analysis of the multiple measurements of deflections due to multiple scattering [CL]

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http://arxiv.org/abs/1706.05863


We revisit the precision of the measurement of track parameters (position, angle) with optimal methods in the presence of detector resolution, multiple scattering and zero magnetic field. We then obtain an optimal estimator of the track momentum by a Bayesian analysis of the filtering innovations of a series of Kalman filters applied to the track.
This work could pave the way to the development of autonomous high-performance gas time-projection chambers (TPC) or silicon wafer gamma-ray space telescopes and be a powerful guide in the optimisation of the design of the multi-kilo-ton liquid argon TPCs that are under development for neutrino studies.

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M. Frosini and D. Bernard
Tue, 20 Jun 17
19/72

Comments: 39 pages, 12 figures, submitted to Nuclear Inst. and Methods in Physics Research, A

Statistical properties of coronal hole rotation rates: Are they linked to the solar interior? [SSA]

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http://arxiv.org/abs/1706.04464


The present paper discusses results of a statistical study of the characteristics of coronal hole (CH) rotation in order to find connections to the internal rotation of the Sun. The goal is to measure CH rotation rates and study their distribution over latitude and their area sizes. In addition, the CH rotation rates are compared with the solar photospheric and inner layer rotational profiles. We study coronal holes observed within $\pm 60$ latitude and longitude degrees from the solar disc centre during the time span from the 1 January 2013 to 20 April 2015, which includes the extended peak of solar cycle 24.We used data created by the Spatial Possibilistic Clustering Algorithm (SPoCA), which provides the exact location and characterisation of solar coronal holes using SDO=AIA 193 {\AA} channel images. The CH rotation rates are measured with four-hour cadence data to track variable positions of the CH geometric centre. North-south asymmetry was found in the distribution of coronal holes: about 60 percent were observed in the northern hemisphere and 40 percent were observed in the southern hemisphere. The smallest and largest CHs were present only at high latitudes. The average sidereal rotation rate for 540 examined CHs is $13:86 (\pm 0:05)$ degrees/d. Conclusions. The latitudinal characteristics of CH rotation do not match any known photospheric rotation profile. The CH angular velocities exceed the photospheric angular velocities at latitudes higher than 35-40 degrees. According to our results, the CH rotation profile perfectly coincides with tachocline and the lower layers of convection zone at around 0.71 $R_{\odot}$; this indicates that CHs may be linked to the solar global magnetic field, which originates in the tachocline region.

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S. Bagashvili, B. Shergelashvili, D. Japaridze, et. al.
Thu, 15 Jun 17
15/68

Comments: 8 pages, 8 figures, Accepted for publication in A&A