THE SYNTHETIC-OVERSAMPLING METHOD: USING PHOTOMETRIC COLORS TO DISCOVER EXTREMELY METAL-POOR STARS

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.