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

Autor:	McBride, Aidan, Lingg, Ryan, Kounkel, Marina, Covey, Kevin, Hutchinson, Brian
Rok vydání:	2020
Předmět:	Astrophysics - Solar and Stellar Astrophysics Astrophysics - Astrophysics of Galaxies
Druh dokumentu:	Working Paper
DOI:	10.3847/1538-3881/ac2432
Popis:	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. Comment: Model available at https://github.com/hutchresearch/Sagitta
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.10463 Zobrazit plný text záznamu View this record from Arxiv