J-PLUS: Identification of low-metallicity stars with artificial neural networks using SPHINX

Abstract

Context. We present a new methodology for the estimation of stellar atmospheric parameters from narrow- and intermediate-band photometry of the Javalambre Photometric Local Universe Survey (J-PLUS), and propose a method for target pre-selection of low-metallicity stars for follow-up spectroscopic studies. Photometric metallicity estimates for stars in the globular cluster M15 are determined using this method. Aims. By development of a neural-network-based photometry pipeline, we aim to produce estimates of effective temperature, T eff , and metallicity, [Fe/H], for a large subset of stars in the J-PLUS footprint. Methods. The Stellar Photometric Index Network Explorer, SPHINX, was developed to produce estimates of T eff and [Fe/H], after training on a combination of J-PLUS photometric inputs and synthetic magnitudes computed for medium-resolution (R ∼ 2000) spectra of the Sloan Digital Sky Survey. This methodology was applied to J-PLUS photometry of the globular cluster M15. Results. Effective temperature estimates made with J-PLUS Early Data Release photometry exhibit low scatter, σ(T eff ) = 91 K, over the temperature range 4500 < T eff (K) < 8500. For stars from the J-PLUS First Data Release with 4500 < T eff (K) < 6200, 85 ± 3% of stars known to have [Fe/H]