[MRS2008] 304.072174-18.911631 , the SIMBAD biblio

[MRS2008] 304.072174-18.911631 , the SIMBAD biblio (9 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.16CEST21:12:38

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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
2008ApJ...678..865M viz 15       D               1 846 92 Evidence for distinct components of the galactic stellar halo from 838 RR Lyrae stars discovered in the LONEOS-I survey. MICELI A., REST A., STUBBS C.W., et al.
2013ApJ...763...32D viz 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.
2013ApJ...765..154D viz 16       D               1 3239 79 Evidence for a Milky way tidal stream reaching beyond 100 kpc. DRAKE A.J., CATELAN M., DJORGOVSKI S.G., et al.
2014MNRAS.441..715G viz 16       D               1 13079 14 A mid-infrared study of RR Lyrae stars with the Wide-field Infrared Survey Explorer all-sky data release. GAVRILCHENKO T., KLEIN C.R., BLOOM J.S., et al.
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.
2017AJ....153..204S viz 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 viz 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 viz 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 viz 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.

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