GUVV J120136.9-033328.1 , the SIMBAD biblio

GUVV J120136.9-033328.1 , the SIMBAD biblio (10 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.19CEST23:27:17

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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
2008AJ....136..259W viz 15       D               1 413 15 The second GALEX ultraviolet variability (GUVV-2) catalog. WHEATLEY J.M., WELSH B.Y. and BROWNE S.E.
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.
2013AJ....146...21S viz 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.
2014ApJ...781...22Z viz 16       D               1 1393 41 La Silla QUEST RR Lyrae star survey: region I. ZINN R., HOROWITZ B., VIVAS A.K., et al.
2014A&A...566A.118D viz 16       D               9 116 25 A comprehensive view of the Virgo stellar stream. DUFFAU S., VIVAS A.K., ZINN R., et al.
2014AJ....148..121K 16       D               1 255 3 The identification of RR Lyrae and δ Scutti stars from variable Galaxy Evolution Explorer ultraviolet sources. KINMAN T.D. and BROWN W.R.
2016ApJ...831..165V viz 16       D               1 469 11 Disentangling the Virgo overdensity with RR Lyrae stars. VIVAS A.K., ZINN R., FARMER 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.
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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