Journal
ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
Volume 246, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.3847/1538-4365/ab55ef
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Funding
- National Key R&D Program of China [2019YFA0405501]
- National Natural Science Foundation of China (NSFC) [11835057]
- National Development and Reform Commission
- Alfred P. Sloan Foundation
- U.S. Department of Energy Office of Science
- Brazilian Participation Group
- Carnegie Institution for Science
- Carnegie Mellon University
- Chilean Participation Group
- French Participation Group
- Harvard-Smithsonian Center for Astrophysics
- Instituto de Astrofisica de Canarias
- Johns Hopkins University
- Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo
- Korean Participation Group
- Lawrence Berkeley National Laboratory
- Leibniz Institut fur Astrophysik Potsdam (AIP)
- Max-Planck-Institut fur Astronomie (MPIA Heidelberg)
- Max-Planck-Institut fur Astrophysik (MPA Garching)
- Max-Planck-Institut fur Extraterrestrische Physik (MPE)
- National Astronomical Observatories of China
- New Mexico State University
- New York University
- University of Notre Dame
- Observatario Nacional/MCTI
- Ohio State University
- Pennsylvania State University
- Shanghai Astronomical Observatory
- United Kingdom Participation Group
- Universidad Nacional Autonoma de Mexico
- University of Arizona
- University of Colorado Boulder
- University of Oxford
- University of Portsmouth
- University of Utah
- University of Virginia
- University of Washington
- University of Wisconsin
- Vanderbilt University
- Yale University
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The LAMOST survey has provided 9 million spectra in its Data Release 5 (DR5) at R similar to 1800. Extracting precise stellar labels is crucial for such a large sample. In this paper, we report the implementation of the Stellar LAbel Machine (SLAM), which is a data-driven method based on support vector regression (SVR), a robust nonlinear regression technique. Thanks to the capability to model highly nonlinear problems with SVR, SLAM can generally derive stellar labels over a wide range of spectral types. This gives it a unique capability compared to other popular data-driven methods. To illustrate this capability, we test the performance of SLAM on stars ranging from T-eff similar to 4000 to similar to 8000 K trained on LAMOST spectra and stellar labels. At g-band signal-to-noise ratio (S/N-g) higher than 100, the random uncertainties of T-eff, log g, and [Fe/H] are 50 K, 0.09 dex, and 0.07 dex, respectively. We then set up another SLAM model trained by APOGEE and LAMOST common stars to demonstrate its capability of dealing with high dimensional problems. The spectra are from LAMOST DR5 and the stellar labels of the training set are from APOGEE DR15, including T-eff, log g, [M/H], [alpha/M], [C/M], and [N/M]. The cross-validated scatters at
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