ESTIMAÇÃO DE PARÂMETROS ESTELARES EM LEVANTAMENTOS FOTOMÉTRICOS MULTIFILTROS COM APRENDIZADO DE MÁQUINA

Abstract

The data produced by large astronomical surveys (photometric and spectroscopic) represent a great challenge and a new way of doing research, based on sophisticated and precise machine learning techniques that, for example, allow new conclusions to be drawn from the observed variables. On the other hand, there is high demand for telescopes and instruments to obtain high-resolution spectra, in order to carry out detailed chemical abundance analysis. Thus, it becomes necessary to select the best potential candidates for a diversity of science cases, based on large area surveys. This work explores the stellar content of the Javalambre Photometric Local Universe Survey (J-PLUS) Data Release 2 and shows its potential for identifying low-metallicity stars using the Stellar Parameters Estimation based on Ensemble Methods (SPEEM) pipeline. SPEEM is a tool tailored to separate stellar sources from quasars and to provide estimations of stellar atmospheric parameters, 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. SPEEM consists of a series of machine-learning models that use a training sample in common between the footprints of J-PLUS and the SEGUE spectroscopic survey. The training sample has stars with temperatures between 4.800 K and 9.000 K, surface gravities between 1.0 and 4.5, and metallicity spans from 3.1 to 0.5. SPEEM has been applied to a sample of stars of J-PLUS/DR2, producing a catalog of stellar parameters, with typical erros of ∆Teff ~ 41 K, ∆logg ~ 0.11 dex, and ∆FeH ~ 0.09 dex, in comparison with SEGUE. A subsample of 177 stars has been identified as potentialy very metal-poor stars, with [Fe/H] ~ 2.5, for spectroscopic follow up. Eleven stars from this subsample were 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. The application of the SPEEM pipeline has been extended to a sample of stars from the fileds of the K2 Mission, observed in open time with the same telescope and camera used in J-PLUS. In this case, SPEEM was modified to include photometric data in the infrared obtained with the satellite WISE and results from the Gaia mission. The training sample was based on results from the Surveys GALAH and APOGEE. The successful application of the pipeline to the K2/T80 sample allows estimating the atmospheric parameters with uncertainties of 92 K for Teff, 0.08 for logg, and 0.12 for [Fe/H]. SPEEM has shown good results from the analysis of the cluster M44, producing reliable estimates of age and metallicity, consistent with the results from the literature. Using SPEEM in combination with the J-PLUS filter system has demonstrated its potential in estimating the stellar atmospheric parameters Teff, logg, and [Fe/H].