文章基本信息

标题：StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization
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作者：Cecilia Garraffo ; Pavlos Protopapas ; Jeremy J.Drake 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2021
卷号：162
期号：4
页码：1-14
DOI：10.3847/1538-3881/ac0ef0
语种：English
出版社：American Institute of Physics
摘要：Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of stellar age and mass enables translating observables into physical stellar parameters by fitting the observed data to synthetic isochrones. However, the complexity of overlapping evolutionary tracks often makes this task numerically challenging, and with a precision that can be highly variable, depending on the area of the parameter space the observation lies in. This work presents StelNet, a Deep Neural Network trained on stellar evolutionary tracks that quickly and accurately predicts mass and age from absolute luminosity and effective temperature for stars with close-to-solar metallicity. The underlying model makes no assumption on the evolutionary stage and includes the pre-main-sequence phase. We use bootstrapping and train many models to quantify the uncertainty of the model. To break the model's intrinsic degeneracy resulting from overlapping evolutionary paths, we also built a hierarchical model that retrieves realistic posterior probability distributions of the stellar mass and age. We further test and train StelNet using a sample of stars with well-determined masses and ages from the literature.