Untangling the Galaxy III: Photometric Search for Pre-main Sequence
  Stars with Deep Learning
  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 2MASS 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 the Gould's Belt.
