http://arxiv.org/abs/1804.09596
We present a new framework (MADE) that produces distance and age estimates by applying a Bayesian isochrone pipeline to a combination of photometric, astrometric and spectroscopic data. For giant stars, the framework can supplement these observational constraints with posterior predictive distributions for mass from a new Bayesian spectroscopic mass estimator. The new mass estimator is a Bayesian artificial neural network (ANN) that learns the relationship between a specified set of inputs and outputs based on a training set. Posterior predictive distributions for the outputs given new inputs are computed, taking into account input uncertainties, and uncertainties in the parameters of the ANN. MADE trains the ANN on stars with spectroscopic and asteroseismology data to enable posterior predictive distributions for present masses of giant stars to be evaluated given spectroscopic data.
We apply MADE to $\sim10\,000$ red giants in the overlap between APO Galactic Evolution Experiment (APOGEE) and the Tycho-Gaia astrometric solution (TGAS). The ANN is trained on a subsample of these stars with new asteroseismology determinations of mass, and is able to predict the masses to a similar degree of uncertainty as the measurement uncertainty. In particular, it is able to reduce the uncertainty on those with the highest measurement uncertainty. Using these masses in the Bayesian isochrone pipeline along with photometric and astrometric data, we are able to obtain distance estimates with uncertainties of order $\sim 10\%$ and age estimates with uncertainties of order $\sim 20\%$. Our resulting catalogue clearly demonstrates the expected thick and thin disc components in the [M/H]-[$\alpha$/M] plane when examined by age.
P. Das and J. Sanders
Thu, 26 Apr 18
2/70
Comments: 12 pages, 3 tables, 3 figures. Submitted to MNRAS
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