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

TL;DR

This paper introduces Sagitta, a deep learning model using Gaia and 2MASS data to identify pre-main sequence stars and estimate their ages, enhancing our understanding of stellar evolution and star formation history.

Contribution

The paper presents a novel neural network model that accurately identifies pre-main sequence stars and estimates their ages using photometric data, revealing new insights into local star-forming regions.

Findings

Successfully recovers known star-forming regions up to 5 kpc

Reveals detailed star formation history in the solar neighborhood

Identifies structures like the Local Bubble and Gould's Belt

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 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.