Untangling the Galaxy. III. Photometric Search for Pre-main-sequence Stars with Deep Learning

Abstract

Abstract A reliable census of pre-main-sequence stars with known ages is critical to our understanding of early stellar evolution, but historically there has been difficulty in separating such stars from the field. We present a trained neural network model, Sagitta, that relies on Gaia DR2 and 2 Micron All-Sky Survey photometry to identify pre-main-sequence stars and to derive their age estimates. Our model successfully recovers populations and stellar properties associated with known star-forming regions up to five kpc. Furthermore, it allows for a detailed look at the star-forming history of the solar neighborhood, particularly at age ranges to which we were not previously sensitive. In particular, we observe several bubbles in the distribution of stars, the most notable of which is a ring of stars associated with the Local Bubble, which may have common origins with Gould’s Belt.