Stellar age determination using deep neural networks: Isochrone ages for 1.3 million stars, based on BaSTI, MIST, PARSEC, Dartmouth and SYCLIST evolutionary grids

TL;DR

This paper presents a deep neural network approach for stellar age determination that is faster and less biased than traditional methods, enabling large-scale galactic archaeology with extensive spectroscopic data.

Contribution

The authors develop a model-driven deep learning method trained on stellar evolutionary grids, providing accurate age estimates with significantly reduced computational cost.

Findings

Achieved median age deviation of 0.20 Gyr compared to literature values.

Demonstrated comparable accuracy to Bayesian methods like SPInS.

Enabled rapid age estimation for large stellar datasets, with a 60,000-fold speedup.

Abstract

We aim to develop a model-driven deep learning approach to age determination, by training neural networks on stellar evolutionary grids. Contrary to the usual data-driven deep learning approach of using prior age estimates as training data, our method has the potential for a wider and less biased range of application. The low computational cost of deep learning methods compared to bayesian isochrone-fitting allows for a broad analysis of large spectroscopic catalogues. We train multilayer perceptrons on different stellar evolutionary grids to map [M/H], MG, (GBP - GRP) to stellar age ${\tau}$. We combine Gaia photometry and parallaxes, metallicities and ${\alpha}$ elements from spectroscopic surveys and extinction maps, which are passed through the neural networks to estimate stellar ages. We apply our method to the LAMOST DR10, GALAH DR3 & DR4 and APOGEE DR17 spectroscopic surveys, for which we estimate the ages using the BaSTI tracks, along with other stellar evolutionary models. We leverage this novel technique to study, for the first time, differences in age estimates from several evolutionary grids applied on very large datasets. In addition, we date 13 open clusters and one globular cluster and find a median absolute deviation with literature ages of 0.20 Gyr. Along with the stellar ages catalogues from our estimates, we release NEST (Neural Estimator of Stellar Times), a python package to estimate stellar age based on this work, as well as a web interface. We show that, when using the same evolutionary grid, our method retrieves the same ages as a bayesian approach like SPInS, for only a fraction of the computational cost, with a 60,000 speedup factor for a typical star. This model-driven deep learning technique thus opens up the way for broad galactic archeology studies on the largest datasets available today and in the near future with upcoming surveys such as 4MOST.