We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which is used to inform a neural network. We obtain spectroscopic-quality parameters for millions of stars that have only been observed photometrically. The typical uncertainties are of the order of 100K in temperature, 0.1 dex in surface gravity, and 0.1 dex in metallicity and the method performs well down to low metallicity, were obtaining reliable results is known to be difficult.
We present the results of the ground-based observing campaign to build the grid of Spectro-Photometric Standard Stars (SPSS) for the absolute flux calibration of data gathered by Gaia, the European Space Agency (ESA) astrometric mission. The spectro-photometric standard stars catalog is characterized by an internal ≅1% accuracy (and sub-percent precision) and it is tied to the CALSPEC Vega and Sirius systems within ≅1%. The final list of SPSS and their flux tables are presented, together with all the quality parameters and associated stellar properties derived from Gaia and the literature. Improvements with respect to the previous SPSS release (Pancino et al. 2021) are discussed, concerning especially the flux accuracy in the red part of the spectrum, above 800 nm. The grid will be used to calibrate Gaia photometry and spectra fluxes in the DR4 and DR5 releases.
We describe the preliminary results of a ground-based observing campaign aimed at building a grid of approximately 200 spectro-photometric standard stars (SPSS), with an internal ≅1% accuracy (and sub-percent precision), tied to CALSPEC Vega and Sirius systems within ≅1%, for the absolute flux calibration of data gathered by Gaia, the European Space Agency (ESA) astrometric mission. The criteria for the selection and a list of candidates are presented, together with a description of the survey's strategy and the adopted data analysis methods. All candidates were also monitored for constancy (within ±5 mmag, approximately). The present version of the grid contains about half of the final sample, it has already reached the target accuracy but the precision will substantially improve with future releases. It will be used to calibrate the Gaia (E)DR3 release of spectra and photometry.
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