V* V485 Ser , the SIMBAD biblio

V* V485 Ser , the SIMBAD biblio (10 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.25CEST05:43:33

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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
2004AJ....127.1158V viz 503 106 The QUEST RR Lyrae survey. I. The first catalog. VIVAS A.K., ZINN R., ABAD C., et al.
2006AJ....132..714V viz 15       D               493 135 The QUEST RR Lyrae survey. II. The halo overdensities in the first catalog. VIVAS A.K. and ZINN R.
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.
2013AJ....146..101P viz 16       D               1 7198 120 Exploring the variable sky with LINEAR. III. Classification of periodic light curves. PALAVERSA L., IVEZIC Z., EYER L., et al.
2014MNRAS.441.1230A viz 16       D               1 5626 16 Newly discovered RR Lyrae stars in the SDSS-Pan-STARRS1-Catalina footprint. ABBAS M.A., GREBEL E.K., MARTIN N.F., et al.
2014ApJS..213....9D viz 16       D               1 46794 385 The Catalina surveys periodic variable star catalog. DRAKE A.J., GRAHAM M.J., DJORGOVSKI S.G., 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.
2019AJ....158..223P viz 17       D               1 3302 27 Kinematics of the Palomar 5 stellar stream from RR Lyrae stars. PRICE-WHELAN A.M., MATEU C., IORIO G., 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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