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

¹ Department of Physics, University of Notre Dame, Notre Dame, IN. 46556, USA
e-mail: dwhitten@nd.edu
² JINA Center for the Evolution of the Elements, East Lansing, USA
³ Departamento de Astronomia, Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
⁴ Centro de Estudios de Física del Cosmos de Aragón (CEFCA) – Unidad Asociada al CSIC, Plaza San Juan, Planta 2, 44001 Teruel, Spain
⁵ Instituto de Astrofísica de Canarias, 38205 La Laguna, Tenerife, Spain
⁶ Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
⁷ Department of Astronomy and Space Science, Chungnam National University, Daejon 34134, Korea
⁸ Observatório Nacional – MCTIC (ON), Rua Gal. José Cristino 77, São Cristóvão, 20921-400, Rio de Janeiro, Brazil
⁹ Centro de Astrobiología (CSIC-INTA), ESAC, Camino Bajo del Castillo S/N, 28692 Villanueva de la Cañada Madrid, Spain
¹⁰ Instituto de Astronomia, Geofísica e Ciências Atmosféricas (IAG), Universidade de São Paulo, Rua do Matão 1226, C. Universitária, São Paulo, 05508-090, Brazil
¹¹ Departamento de Física, Universidade Federal de Sergipe, Av. Marechal Rondon, s/n, 49000-000 São Cristóvão, SE, Brazil
¹² Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Plaza San Juan, 1, 44001, Teruel, Spain
¹³ Department of Astronomy, University of Michigan, 1085 S. Ave, University Ann Arbor, MI 48109, USA
¹⁴ Department of Phys. & Astronomy, University of Alabama, Gallalee Hall, Tuscaloosa, AL 35401, USA
¹⁵ X-ray Astrophysics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
¹⁶ Department of Physics, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA

Received: 3 May 2018
Accepted: 5 November 2018

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] < −2.0 are recovered by SPHINX. A mean metallicity of [Fe/H] = − 2.32 ± 0.01, with a residual spread of 0.3 dex, is determined for M15 using J-PLUS photometry of 664 likely cluster members.

Conclusions. We confirm the performance of SPHINX within the ranges specified, and verify its utility as a stand-alone tool for photometric estimation of effective temperature and metallicity, and for pre-selection of metal-poor spectroscopic targets.