期刊
ASTROPHYSICAL JOURNAL
卷 811, 期 1, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/0004-637X/811/1/30
关键词
methods: data analysis; methods: statistical; stars: general; stars: statistics; stars: fundamental parameters; surveys
资金
- NASA from a Hubble Fellowship [HST-HF-51325.01]
- STScI
- NASA [NAS 5-26555]
- Alfred P. Sloan Foundation
- National Science Foundation
- U. S. Department of Energy Office of Science
- University of Arizona
- Brazilian Participation Group
- Brookhaven National Laboratory
- Carnegie Mellon University
- University of Florida
- French Participation Group
- German Participation Group, Harvard University
- Instituto de Astrofisica de Canarias
- Michigan State/Notre Dame/JINA Participation Group
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- Max Planck Institute for Astrophysics
- Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University
- Ohio State University
- Pennsylvania State University
- University of Portsmouth
- Princeton University
- Spanish Participation Group
- University of Tokyo
- University of Utah
- Vanderbilt University
- University of Virginia
- University of Washington
- Yale University
Extremely metal-poor (EMP) stars ([Fe/H] <= -3.0 dex) provide a unique window into understanding the first generation of stars and early chemical enrichment of the universe. EMP stars are exceptionally rare, however, and the relatively small number of confirmed discoveries limits our ability to exploit these near-field probes of the first similar to 500 Myr after the Big Bang. Here, a new method to photometrically estimate [Fe/H] from only broadband photometric colors is presented. I show that the method, which utilizes machine-learning algorithms and a training set of similar to 170,000 stars with spectroscopically measured [Fe/H], produces a typical scatter of similar to 0.29 dex. This performance is similar to what is achievable via low-resolution spectroscopy, and outperforms other photometric techniques, while also being more general. I further show that a slight alteration to the model, wherein synthetic EMP stars are added to the training set, yields the robust identification of EMP candidates. In particular, this synthetic-oversampling method recovers similar to 20% of the EMP stars in the training set, at a precision of similar to 0.05. Furthermore, similar to 65% of the false positives from the model are very metal-poor stars ([Fe/H] <= -2.0 dex). The synthetic-oversampling method is biased toward the discovery of warm (similar to F-type) stars, a consequence of the targeting bias from the Sloan Digital Sky Survey/Sloan Extension for Galactic Understanding survey. This EMP selection method represents a significant improvement over alternative broadband optical selection techniques. The models are applied to >12 million stars, with an expected yield of similar to 600 new EMP stars, which promises to open new avenues for exploring the early universe.
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