Disentangled Representation Learning for Astronomical Chemical Tagging

TL;DR

This paper introduces a neural network-based method to disentangle chemical information from stellar spectra, enabling more accurate chemical tagging without relying on synthetic models, thus improving understanding of galactic formation history.

Contribution

The authors develop a neural network architecture that learns to isolate chemical factors in stellar spectra without prior elemental abundance knowledge, advancing data-driven chemical tagging methods.

Findings

Neural network outperforms simpler models in disentangling spectra.

Disentangled spectra enable chemical tagging without synthetic models.

Performance declines with lower signal-to-noise ratios.

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 are 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. \teff, \logg, \feh). 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 non-chemical 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 dataset. 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.