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

@article{McBride2021UntanglingTG,
  title={Untangling the Galaxy. III. Photometric Search for Pre-main-sequence Stars with Deep Learning},
  author={Aidan McBride and Ryan T. Lingg and Marina A. Kounkel and Kevin R. Covey and Brian Hutchinson},
  journal={The Astronomical Journal},
  year={2021},
  volume={162}
}
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… 
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