Machine learning metallicity predictions using SDSS

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 ~500Myr 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 ~170000 stars with spectroscopically measured [Fe/H], produces a typical scatter of ~0.29dex. 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.