LINEAR 18833436 , the SIMBAD biblio

LINEAR 18833436 , the SIMBAD biblio (10 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.19CEST11:31:36


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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.
2011RAA....11..833L 15       D               1 40 2 Spectroscopic and photometric observations of unidentified ultraviolet variable objects in the GUVV-2 Catalog. LI Y., WANG J., WEI J.-Y., 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.
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.
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.
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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