arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.08390astro-ph.SRastro-ph.GA
收藏

APOGEE Net: Improving the derived spectral parameters for young stars through deep learning

Richard Olney, Marina Kounkel, Chad Schillinger, Matthew T. Scoggins, Yichuan Yin, Erin Howard, K. R. Covey, Brian Hutchinson, Keivan G. Stassun

英文摘要:

Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main sequence or evolved stars, where reliable synthetic spectra provide labels and data for training. Spectral models of young stellar objects (YSOs) and low mass main sequence (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 log g, Teff, and Fe/H for stars with APOGEE spectra in the DR14 dataset. This "APOGEE Net" has produced reliable predictions of log g for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-main sequence evolutionary tracks, and correlate well with independently derived stellar radii. These values will be useful for studying pre-main sequence stellar populations to accurately diagnose membership and ages.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑