Abstract
We present a new approach (MADE) that generates mass, age, and distance estimates of red giant stars from a combination of astrometric, photometric, and spectroscopic data. The core of the approach is a Bayesian artificial neural network (ANN) that learns from and completely replaces stellar isochrones. The ANN is trained using a sample of red giant stars with mass estimates from asteroseismology. A Bayesian isochrone pipeline uses the astrometric, photometric, spectroscopic, and asteroseismology data to determine posterior distributions for the training outputs: mass, age, and distance. Given new inputs, posterior predictive distributions for the outputs are computed, taking into account both input uncertainties, and uncertainties in the ANN parameters. We apply MADE to similar to 10 000 red giants in the overlap between the 14th data release from the APO Galactic Evolution Experiment (APOGEE) and the Tycho-Gaia astrometric solution (TEAS). The ANN is able to reduce the uncertainty on mass, age, and distance estimates for training-set stars with high output uncertainties allocated through the Bayesian isochrone pipeline. The fractional uncertainties on mass are < 10 per cent and on age are between 10 to 25 per cent. Moreover, the time taken for our ANN to predict masses, ages, and distances for the entire catalogue of APOGEE-TGAS stars is of a similar order of the time taken by the Bayesian isochrone pipeline to run on a handful of stars. Our resulting catalogue clearly demonstrates the expected thick- and thin-disc components in the [M/H]-[alpha /M] plane, when examined by age.