Corpus ID: 229340257

Untangling the Galaxy III: Photometric Search for Pre-main Sequence Stars with Deep Learning

@inproceedings{McBride2020UntanglingTG,
  title={Untangling the Galaxy III: Photometric Search for Pre-main Sequence Stars with Deep Learning},
  author={Aidan McBride and Ryan T Lingg and M. Kounkel and K. Covey and Brian Hutchinson},
  year={2020}
}

• Aidan McBride, Ryan T Lingg, +2 authors Brian Hutchinson
• Published 2020
• Physics

A reliable census of 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… Expand

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