Disentangled Representation Learning for Astronomical Chemical Tagging

The Astrophysical Journal, vol. 913, pp. 12

Abstract

Abstract Modern astronomical surveys are observing spectral data for millions of stars. These spectra contain chemical information that can be used to trace the Galaxy’s formation and chemical enrichment history. However, extracting the information from spectra and making precise and accurate chemical abundance measurements is challenging. Here we present a data-driven method for isolating the chemical factors of variation in stellar spectra from those of other parameters (i.e., T eff, log g, [Fe/H]). This enables us to build a spectral projection for each star with these parameters removed. We do this with no ab initio knowledge of elemental abundances themselves and hence bypass the uncertainties and systematics associated with modeling that rely on synthetic stellar spectra. To remove known nonchemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation. We simulate our recovery of chemically identical stars using the disentangled spectra in a synthetic APOGEE-like data set. We show that this recovery declines as a function of the signal-to-noise ratio but that our neural network architecture outperforms simpler modeling choices. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging.

Authors 4

1. Damien de Mijolla corresponding

   University College London

   Affiliation as printed

   Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, UK; ucapdde@ucl.ac.uk

   Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, UK

2. Melissa Ness

   Flatiron Institute · Columbia University

   Affiliation as printed

   Center for Computational Astrophysics, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA

   Department of Astronomy, Columbia University, Pupin Physics Laboratories, New York, NY 10027, USA

3. S. Viti Aachen Leiden Observatory

   Leiden University · Leiden Observatory · University College London

   Affiliation as printed

   Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, UK; ucapdde@ucl.ac.uk

   Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, Netherlands

   Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, UK

4. Adam Wheeler

   Columbia University

   Affiliation as printed

   Department of Astronomy, Columbia University, Pupin Physics Laboratories, New York, NY 10027, USA