Disentangled Representation Learning for Astronomical Chemical Tagging

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.

Automated summary

Modern astronomical surveys are using spectral data from millions of stars to trace the Galaxy's formation and chemical enrichment history, but extracting chemical information from spectra and making accurate abundance measurements is challenging. A data-driven method can isolate chemical factors in stellar spectra and build a spectral projection with parameters removed, using a neural network architecture to learn a disentangled spectral representation. This approach demonstrates the feasibility of data-driven chemical tagging without relying on prior knowledge of elemental abundances.