论文标题
解开银河系III：使用深度学习的前序列序列的光度搜索
Untangling the Galaxy III: Photometric Search for Pre-main Sequence Stars with Deep Learning
论文作者
论文摘要
具有已知年龄的前序列恒星的可靠人口普查对于我们对早期恒星进化的理解至关重要，但是从历史上看，将这种恒星与该领域分开困难。我们提出了训练有素的神经网络模型Sagitta，该模型依赖于Gaia DR2和2MASS光度法来识别前序列序列星并得出其年龄估计。我们的模型成功地恢复了与已知的恒星形成区域相关的人群和恒星特性，最多五个kpc。此外，它允许详细介绍太阳街区的星形历史，尤其是在我们以前不敏感的年龄范围内。特别是，我们观察到恒星分布的几个气泡，其中最值得注意的是与局部气泡相关的恒星环，这可能与古尔德的皮带具有共同的起源。
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.
