VizieR Online Data Catalog: Dynamical heating across the Milky Way disc (Mackereth+, 2019)

We use a catalogue of stellar positions, velocities, element abundances, and estimated ages, comprising a cross-match of the catalogues from the 14th data release (DR14; Abolfathi et al. 2018ApJS..235...42A) of the SDSS-IV APOGEE-2 survey, and the second data release (DR2; Gaia Collaboration 2018A&A...616A...1G, Cat. I/345) of the ESA-Gaia mission. Ages are estimated from a neural network based model trained on data from the APOKASC catalogue, which contains stars observed both spectroscopically by APOGEE and asteroseismically by the Kepler mission.

Our method uses a BCNN, implemented in the astroNNpython package (Leung & Bovy 2019MNRAS.483.3255L), which wraps the Keras and TensorFlow machine learning architectures. BCNNs treat the more commonly used convolutional neural network (CNN) as a Bayesian regression problem, inferring the probability distributions over the model weights.

The training data is compiled and loaded using functions in astroNN. Individual APOGEE visit spectra are recombined and continuum normalized using a method similar to that used in the Cannon (Ness et al. 2015ApJ...808...16N, Cat. J/ApJ/808/16), which makes a Chebyshev polynomial fit to specifically selected pixels separately between the different CCD chips in APOGEE. This procedure results in an improved normalization, which is preferable when training the BCNN model, over the normalization used in the standard APOGEE data reduction.

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