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GSC 02548-00251 , the SIMBAD biblio (9 results) | C.D.S. - SIMBAD4 rel 1.8 - 2024.04.25CEST18:14:22 |
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 |
---|---|---|---|---|---|---|---|---|---|
2000AJ....120.2065B | 268 | 117 | A search for stars of very low metal abundance. V. Photoelectric UBV photometry of metal-weak candidates from the northern HK survey. | BONIFACIO P., MONAI S. and BEERS T.C. | |||||
2004A&A...422..527S | 500 | 36 | uvby-β photometry of high-velocity and metal-poor stars. X. Stars of very low metal abundance: Observations, reddenings, metallicities, classifications, distances, and relative ages. | SCHUSTER W.J., BEERS T.C., MICHEL R., et al. | |||||
2009PZP.....9....2K | O | 8 | 0 | Eight new RR Lyrae variables. | KHRUSLOV A.V. | ||||
2013ApJ...763...32D | 16 | D | 1 | 12288 | 202 | Probing the outer galactic halo with RR Lyrae from the Catalina surveys. | DRAKE A.J., CATELAN M., DJORGOVSKI S.G., et al. | ||
2013AJ....146...21S | 16 | D | 1 | 5702 | 98 | Exploring the variable sky with LINEAR. II. Halo structure and substructure traced by RR Lyrae stars to 30 kpc. | SESAR B., IVEZIC Z., STUART J.S., et al. | ||
2017AJ....153..204S | 16 | D | 1 | 46977 | 123 | Machine-learned identification of RR Lyrae stars from sparse, multi-band data: the PS1 sample. | SESAR B., HERNITSCHEK N., MITROVIC S., et al. | ||
2018AJ....156..241H | 16 | D | 1 | 311114 | 199 | A first catalog of variable stars measured by the Asteroid Terrestrial-impact Last Alert System (ATLAS). | HEINZE A.N., TONRY J.L., DENNEAU L., et al. | ||
2019A&A...622A..60C | 17 | 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. | ||
2022ApJS..261...33D | 18 | D | 1 | 104673 | 3 | Photometric Metallicity Prediction of Fundamental-mode RR Lyrae Stars in the Gaia Optical and Ks Infrared Wave Bands by Deep Learning. | DEKANY I. and GREBEL E.K. |