ATO J123.1099+03.5555 , the SIMBAD biblio

ATO J123.1099+03.5555 , the SIMBAD biblio (9 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.24CEST02:22:17

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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.
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...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.
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.
2021ApJ...912..144M viz 17       D               1 2105 23 Metallicity of galactic RR Lyrae from optical and infrared light curves. I. Period-Fourier-Metallicity relations for fundamental-mode RR Lyrae. MULLEN J.P., MARENGO M., MARTINEZ-VAZQUEZ C.E., 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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