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.