文章基本信息

标题：APOGEE Net: Improving the Derived Spectral Parameters for Young Stars through Deep Learning
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作者：Richard Olney ; Marina Kounkel ; Chad Schillinger 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2020
卷号：159
期号：4
页码：2546-2563
DOI：10.3847/1538-3881/ab7a97
语种：English
出版社：American Institute of Physics
摘要：Machine learning allows for efficient extraction of physical properties from stellar spectra that have been obtained by large surveys.The viability of machine-learning approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main-sequence (MS) or evolved stars, where reliable synthetic spectra provide labels and data for training.Spectral models of young stellar objects (YSOs) and low-mass MS stars are less well-matched to their empirical counterparts, however, posing barriers to previous approaches to classify spectra of such stars.In this work, we generate labels for YSOs and low-mass MS stars through their photometry.We then use these labels to train a deep convolutional neural network to predict $\mathrm{log}g$, Teff, and Fe/H for stars with Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra in the DR14 data set.This "APOGEE Net" has produced reliable predictions of $\mathrm{log}g$ for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-MS evolutionary tracks, and it correlates well with independently derived stellar radii.These values will be useful for studying pre-MS stellar populations to accurately diagnose membership and ages.
关键词：Astroinformatics;Computational methods;Young stellar objects;Low mass stars;Stellar classification