# Inferring Galactic magnetic field model parameters using IMAGINE – An Interstellar MAGnetic field INference Engine [IMA]

Context. The Galactic magnetic field (GMF) has a huge impact on the evolution of the Milky Way. Yet currently there exists no standard model for it, as its structure is not fully understood. In the past many parametric GMF models of varying complexity have been developed that all have been fitted to an individual set of observational data complicating comparability. Aims. Our goal is to systematize parameter inference of GMF models. We want to enable a statistical comparison of different models in the future, allow for simple refitting with respect to newly available data sets and thereby increase the research area’s transparency. We aim to make state-of-the-art Bayesian methods easily available and in particular to treat the statistics related to the random components of the GMF correctly. Methods. To achieve our goals, we built IMAGINE, the Interstellar Magnetic Field Inference Engine. It is a modular open source framework for doing inference on generic parametric models of the Galaxy. We combine highly optimized tools and technology such as the MultiNest sampler and the information field theory framework NIFTy in order to leverage existing expertise. Results. We demonstrate the steps needed for robust parameter inference and model comparison. Our results show how important the combination of complementary observables like synchrotron emission and Faraday depth is while building a model and fitting its parameters to data. IMAGINE is open-source software available under the GNU General Public License v3 (GPL-3) at: https://gitlab.mpcdf.mpg.de/ift/IMAGINE

T. Steininger, T. Ensslin, M. Greiner, et. al.
Tue, 16 Jan 18
34/79

|

# Automated Spectral Classification of Galaxies using Machine Learning Approach on Alibaba Cloud AI platform (PAI) [IMA]

Automated spectral classification is an active research area in astronomy at the age of data explosion. While new generation of sky survey telescopes (e.g. LAMOST and SDSS) produce huge amount of spectra, automated spectral classification is highly required to replace the current model fitting approach with human intervention. Galaxies, and especially active galactic nucleus (AGNs), are important targets of sky survey programs. Efficient and automated methods for galaxy spectra classification is the basis of systematic study on physical properties and evolution of galaxies. To address the problem, in this paper we carry out an experiment on Alibaba Cloud AI plaform (PAI) to explore automated galaxy spectral classification using machine learning approach. Supervised machine learning algorithms (Logistic Regression, Random Forest and Linear SVM) were performed on a dataset consist of ~ 10000 galaxy spectra of SDSS DR14, and the classification results of which are compared and discussed. These galaxy spectra each has a subclass tag (i.e. AGNs, Starburst, Starforming, and etc.) that we use as training labels.

Y. Tao, Y. Zhang, C. Cui, et. al.
Tue, 16 Jan 18
39/79

|

# Radio Galaxy Zoo: Compact and extended radio source classification with deep learning [IMA]

Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. The current work aims to establish when multiple components are present, in the astronomical context of synthesis imaging observations of radio sources. To this effect, we design a convolutional neural network to differentiate between different morphology classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factors that affect the performance of such neural networks, such as the amount of training data, number and nature of layers and the hyperparameters. We begin with a simple experiment in which we only differentiate between two extreme morphologies, using compact and multiple component extended sources. We found that a three convolutional layer architecture yielded very good results, achieving a classification accuracy of 97.4% on a test data set. The same architecture was then tested on a four-class problem where we let the network classify sources into compact and three classes of extended sources, achieving a test achieving a test accuracy of 93.5%. The best-performing convolutional neural network setup has been verified against RGZ Data Release 1 where a final test accuracy of 94.8% was obtained, using both original and augmented images. The use of sigma clipping does not offer a significant benefit overall, except in cases with a small number of training images.

V. Lukic, M. Bruggen, J. Banfield, et. al.
Tue, 16 Jan 18
58/79

Comments: 17 pages, 11 figures. Accepted for publication in MNRAS

|

# SETI is Part of Astrobiology [IMA]

“Traditional SETI is not part of astrobiology” declares the NASA Astrobiology Strategy 2015 document. This is incorrect. In this white paper, I argue that SETI$-$seen as the search for technosignatures characteristic of the future of life in the universe$-$is a neglected complement to the search for biosignatures in NASA’s astrobiology portfolio, and may offer the more fruitful avenue to the discovery of life elsewhere in the universe, as recognized by the Astro2010 decadal survey. I rebut six erroneous perceptions that may contribute to the field’s absence from NASA’s astrobiology strategy, and argue that since SETI is, quite obviously, part of astrobiology, SETI practitioners should at the very least be expressly encouraged to compete on a level playing field with practitioners of other subfields for NASA astrobiology resources.

J. Wright
Tue, 16 Jan 18
65/79

Comments: 5 pages, submitted as a white paper to the National Academies of Sciences, Engineering, and Medicine ad hoc Committee on Astrobiology Science Strategy for Life in the Universe, 2018. this http URL

|

# Impact of infrasound atmospheric noise on gravity detectors used for astrophysical and geophysical applications [IMA]

Density changes in the atmosphere produce a fluctuating gravity field that affect gravity strainmeters or gravity gradiometers used for the detection of gravitational-waves and for geophysical applications. This work addresses the impact of the atmospheric local gravity noise on such detectors, extending previous analyses. In particular we present the effect introduced by the building housing the detectors, and we analyze local gravity-noise suppression by constructing the detector underground. We present also new sound spectra and correlations measurements. The results obtained are important for the design of future gravitational-wave detectors and gravity gradiometers used to detect prompt gravity perturbations from earthquakes.

D. Fiorucci, J. Harms, M. Barsuglia, et. al.
Tue, 16 Jan 18
67/79

|

# The path towards high-contrast imaging with the VLTI: the Hi-5 project [IMA]

The development of high-contrast capabilities has long been recognized as one of the top priorities for the VLTI. As of today, the VLTI routinely achieves contrasts of a few 10$^{-3}$ in the near-infrared with PIONIER (H band) and GRAVITY (K band). Nulling interferometers in the northern hemisphere and non-redundant aperture masking experiments have, however, demonstrated that contrasts of at least a few 10$^{-4}$ are within reach using specific beam combination and data acquisition techniques. In this paper, we explore the possibility to reach similar or higher contrasts on the VLTI. After reviewing the state-of-the-art in high-contrast infrared interferometry, we discuss key features that made the success of other high-contrast interferometric instruments (e.g., integrated optics, nulling, closure phase, and statistical data reduction) and address possible avenues to improve the contrast of the VLTI by at least one order of magnitude. In particular, we discuss the possibility to use integrated optics, proven in the near-infrared, in the thermal near-infrared (L and M bands, 3-5 $\mu$m), a sweet spot to image and characterize young extra-solar planetary systems. Finally, we address the science cases of a high-contrast VLTI imaging instrument and focus particularly on exoplanet science (young exoplanets, planet formation, and exozodiacal disks), stellar physics (fundamental parameters and multiplicity), and extragalactic astrophysics (active galactic nuclei and fundamental constants). Synergies and scientific preparation for other potential future instruments such as the Planet Formation Imager are also briefly discussed.

D. Defrere, O. Absil, J. Berger, et. al.
Mon, 15 Jan 18
33/59

Comments: 24 pages, 2 figures, submitted to Experimental Astronomy

|

# A recent history of science cases for interferometry [IMA]

Optical long-baseline interferometry is a unique and powerful technique for astronomical research. Since 2004, optical interferometers have produced an increasing number of scientific papers covering various fields of astrophysics. As current interferometric facilities are reaching their maturity, we take the opportunity in this paper to summarize the conclusions of a few key meetings, workshops, and conferences dedicated to interferometry. We present the most persistent recommendations related to science cases and discuss some key technological developments required to address them. In the era of extremely large telescopes, optical long-baseline interferometers will remain crucial to probe the smallest spatial scales and make breakthrough discoveries.

D. Defrere, C. Aerts, M. Kishimoto, et. al.
Mon, 15 Jan 18
46/59

Comments: 11 pages, 1 figure, submitted to Experimental Astronomy

|