J-PLUS: Searching for very metal-poor star candidates using the SPEEM pipeline
Context. We explore the stellar content of the Javalambre Photometric Local Universe Survey (J-PLUS) Data Release 2 and show its potential for identifying low-metallicity stars using the Stellar Parameters Estimation based on Ensemble Methods (SPEEM) pipeline. Aims. SPEEM is a tool used to provide determinations of atmospheric parameters for stars and separate stellar sources from quasars based on the unique J-PLUS photometric system. The adoption of adequate selection criteria allows for the identification of metal-poor star candidates that are suitable for spectroscopic follow-up investigations. Methods. SPEEM consists of a series of machine-learning models that use a training sample observed by both J-PLUS and the SEGUE spectroscopic survey. The training sample has temperatures, Teff, between 4800 K and 9000 K, values of log g between 1.0 and 4.5, as well as -3.1 < [Fe/H] < +0.5. The performance of the pipeline was tested with a sample of stars observed by the LAMOST survey within the same parameter range. Results. The average differences between the parameters of a sample of stars observed with SEGUE and J-PLUS, obtained with the SEGUE Stellar Parameter Pipeline and SPEEM, respectively, are ΔTeff ∼ 41 K, Δlog g ∼ 0.11 dex, and Δ[Fe/H] ∼ 0.09 dex. We define a sample of 177 stars that have been identified as new candidates with [Fe/H] < -2.5, with 11 of them having been observed with the ISIS spectrograph at the William Herschel Telescope. The spectroscopic analysis confirms that 64% of stars have [Fe/H] < -2.5, including one new star with [Fe/H] < -3.0. Conclusions. Using SPEEM in combination with the J-PLUS filter system has demonstrated their potential in estimating the stellar atmospheric parameters (Teff, log g, and [Fe/H]). The spectroscopic validation of the candidates shows that SPEEM yields a success rate of 64% on the identification of very metal-poor star candidates with [Fe/H] < -2.5. © 2021 ESO. ; Based on observations made with the JAST80 telescope at the Observatorio Astrofísico de Javalambre (OAJ), in Teruel, owned, managed, and operated by the Centro de Estudios de Física del Cosmos de Aragón. We acknowledge the OAJ Data Processing and Archiving Unit (UPAD) for reducing the OAJ data used in this work. Funding for the J-PLUS Project has been provided by the Governments of Spain and Aragón through the Fondo de Inversiones de Teruel; the Aragón Government through the Reseach Groups E96, E103, and E16_17R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grants PGC2018-097585-B-C21 and PGC2018-097585-B-C22; the Spanish Ministry of Economy and Competitiveness (MINECO) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). The Brazilian agencies FINEP, FAPESP, and the National Observatory of Brazil have also contributed to this project. C.A.G. acknowledges financial support from the CAPES through scholarship for developing his PhD project, and extend and special mention to Nathaniel Tucker for introducing him to the amazing world of machine learning. The work of V.M.P. is supported by NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. Y.S.L. acknowl edges support from the National Research Foundation (NRF) of Korea grant funded by the Ministry of Science and ICT (NRF-2021R1A2C1008679). F.J.E. acknowledges financial support from the Spanish MINECO/FEDER through the grant AYA2017-84089 and MDM-2017-0737 at Centro de Astrobiología (CSIC-INTA), Unidad de Excelencia María de Maeztu, and from the European Union's Horizon 2020 research and innovation programme under Grant Agreement no. 824064 through the ESCAPE – The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures project. R.A.D. acknowledges support from the CNPq through BP grant 308105/2018-4. This research has made use of the Spanish Virtual Observatory (http://svo.cab.inta-csic.es) supported from the Spanish MICINN/FEDER through grant AyA2017-84089. This research made use of Matplotlib, a 2D graphics package used for Python for publication-quality image generation across user interfaces and operating systems (Hunter 2007). ; Peer reviewed