The Synthetic-Oversampling Method: Using Photometric Colors to Discover Extremely Metal-Poor Stars

Abstract

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 ~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 ~170,000 stars with spectroscopically measured [Fe/H], produces a typical scatter of ~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 ~20% of the EMP stars in the training set, at a precision of ~0.05. Furthermore, ~65% of the false positives from the model are very metal-poor stars ([Fe/H] < -2.0 dex). The synthetic-oversampling method is biased towards the discovery of warm (~F-type) stars, a consequence of the targeting bias from the SDSS/SEGUE 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 ~600 new EMP stars, which promises to open new avenues for exploring the early universe.

15 pages, 7 figures, to be submitted to ApJ

References in corpus (17)

• The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
• The Milky Way Tomography with SDSS: II. Stellar Metallicity
• Near-Field Cosmology with Metal-Poor Stars
• The SEGUE Stellar Parameter Pipeline. I. Description and Initial Validation Tests
• A Search for Stars of Very Low Metal Abundance. VI. Detailed Abundances of 313 Metal-Poor Stars
• The SEGUE Stellar Parameter Pipeline. II. Validation with Galactic Globular and Open Clusters
• The SEGUE Stellar Parameter Pipeline. III. Comparison with High-Resolution Spectroscopy of SDSS/SEGUE Field Stars
• The stellar content of the Hamburg/ESO survey. IV. Selection of candidate metal-poor stars
• Bright Metal-Poor Stars from the Hamburg/ESO Survey. I. Selection and Follow-up Observations from 329 Fields
• High-Resolution Spectroscopic Study of Extremely Metal-Poor Star Candidates from the SkyMapper Survey
• The Best and Brightest Metal-Poor Stars
• Deep SDSS optical spectroscopy of distant halo stars I. Atmospheric parameters and stellar metallicity distribution
• Stellar color regression: a spectroscopy based method for color calibration to a few mmag accuracy and the recalibration of Stripe 82
• A Machine Learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves
• Spectroscopic versus Photometric Metallicities: Milky Way Dwarf Spheroidal Companions as a Test Case
• Stellar loci III: Photometric metallicities for half million FGK stars of Stripe 82
• Exploring the Variable Sky with the Sloan Digital Sky Survey

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