Cycle-StarNet: Bridging the Gap between Theory and Data by Leveraging Large Data Sets

Abstract

Abstract Advancements in stellar spectroscopy data acquisition have made it necessary to accomplish similar improvements in efficient data analysis techniques. Current automated methods for analyzing spectra are either (a) data driven, which requires prior knowledge of stellar parameters and elemental abundances, or (b) based on theoretical synthetic models that are susceptible to the gap between theory and practice. In this study, we present a hybrid generative domain-adaptation method that turns simulated stellar spectra into realistic spectra by applying unsupervised learning to large spectroscopic surveys. We apply our technique to the APOGEE H-band spectra at R = 22,500 and the Kurucz synthetic models. As a proof of concept, two case studies are presented. The first is the calibration of synthetic data to become consistent with observations. To accomplish this, synthetic models are morphed into spectra that resemble observations, thereby reducing the gap between theory and observations. Fitting the observed spectra shows an improved average reduced from 1.97 to 1.22, along with a mean residual reduced from 0.16 to −0.01 in normalized flux. The second case study is the identification of the elemental source of missing spectral lines in the synthetic modeling. A mock data set is used to show that absorption lines can be recovered when they are absent in one of the domains. This method can be applied to other fields that use large data sets and are currently limited by modeling accuracy. The code used in this study is made publicly available on GitHub (https://github.com/teaghan/Cycle_SN).