CRTS J053957.0-470557 , the SIMBAD biblio

CRTS J053957.0-470557 , the SIMBAD biblio (9 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.18CEST14:36:17

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Bibcode/DOI Score in Title|Abstract|
Keywords 		in a table in teXt, Caption, ... Nb occurence Nb objects in ref Citations
(from ADS) 		Title First 3 Authors
2015MNRAS.446.2251T viz 16       D               1 10532 100 Discovery of ∼9,000 new RR Lyrae in the southern Catalina surveys. TORREALBA G., CATELAN M., DRAKE A.J., et al.
2019A&A...622A..60C viz 16       D               1 150347 194 Gaia Data Release 2. Specific characterisation and validation of all-sky Cepheids and RR Lyrae stars. CLEMENTINI G., RIPEPI V., MOLINARO R., et al.
2019AJ....158...16S viz 16       D               1 5775 ~ Identification of RR Lyrae stars in multiband, sparsely sampled data from the Dark Energy Survey using template fitting and random forest classification. STRINGER K.M., LONG J.P., MACRI L.M., et al.
2020A&A...635A.155M viz 16       D               1 812 ~ The Gaia-ESO Survey: detection and characterisation of single-line spectroscopic binaries. MERLE T., VAN DER SWAELMEN M., VAN ECK S., et al.
2021ApJ...911..109S viz 17       D               1 7001 17 Identifying RR Lyrae variable stars in six years of the Dark Energy Survey. STRINGER K.M., DRLICA-WAGNER A., MACRI L., et al.
2023ApJ...944...88L viz 17       D               1 135907 6 Photometric Metallicity and Distance Estimates for ∼136,000 RR Lyrae Stars from Gaia Data Release 3. LI X.-Y., HUANG Y., LIU G.-C., et al.
2023A&A...674A..18C viz 17       D               1 271810 30 Gaia Data Release 3 Specific processing and validation of all-sky RR Lyrae and Cepheid stars: The RR Lyrae sample. CLEMENTINI G., RIPEPI V., GAROFALO A., et al.
2023A&A...676A.129H viz 17       D               1 114943 ~ The Gaia-ESO Survey: Homogenisation of stellar parameters and elemental abundances. HOURIHANE A., FRANCOIS P., WORLEY C.C., et al.
2024A&A...692A.228C viz 18       D               1 49864 ~ Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks. CANDEBAT N., SACCO G.G., MAGRINI L., et al.

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