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
