V* V366 Lyr , the SIMBAD biblio

V* V366 Lyr , the SIMBAD biblio (23 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.05.14CEST12:09:05


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Title First 3 Authors
1966AN....289..139H 352 47 Mitteilungen uber neuentdeckte veraenderliche Sterne. HOFFMEISTER C.
1968IBVS..311....1K 1646 13 Identification list of the new variable stars nominated in 1968 (an extract from the 56th nominating list) KUKARKIN B.V., KHOLOPOV P.N., EFREMOV Y.N., et al.
1999IBVS.4764....1K 248 2 Precise coordinates of variable stars. (3). KATO T.
2010MNRAS.409.1585B 283       D     X C F     6 33 121 Flavours of variability: 29 RR Lyrae stars observed with Kepler. BENKO J.M., KOLENBERG K., SZABO R., et al.
2012AJ....144...39L viz 15       D               1 144 20 The all-sky GEOS RR Lyr survey with the TAROT telescopes: analysis of the Blazhko effect. LE BORGNE J.-F., KLOTZ A., PORETTI E., et al.
2012MNRAS.426.2413B 15       D               2 62 18 A search for SX Phe stars among Kepler δ Scuti stars. BALONA L.A. and NEMEC J.M.
2013A&A...549A.101S viz 16       D               1 242 26 Known Galactic field Blazhko stars. SKARKA M.
2013ApJ...773..181N 16       D               7 68 131 Metal abundances, radial velocities, and other physical characteristics for the RR Lyrae stars in the Kepler field. NEMEC J.M., COHEN J.G., RIPEPI V., 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.
2014ApJS..213...31B viz 709     A D S   X C       17 22 60 Long-timescale behavior of the Blazhko effect from rectified Kepler data. BENKO J.M., PLACHY E., SZABO R., et al.
2016ApJ...829...23D viz 16       D               1 4044 212 The Kepler catalog of stellar flares. DAVENPORT J.R.A.
2016ApJS..227...30N 16       D               4 47 4 The Palomar Transient Factory and RR Lyrae: the metallicity-light curve relation based on ab-type RR Lyrae in the Kepler field. NGEOW C.-C., YU P.-C., BELLM E., 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.
2018MNRAS.473..412B 16       D               1 37 3 On the connection between almost periodic functions and Blazhko light curves. BENKO J.M.
2018ApJ...864...57M viz 16       D               1 159 5 Chemical compositions of field and globular cluster RR Lyrae stars. I. NGC 3201. MAGURNO D., SNEDEN C., BRAGA V.F., et al.
2018AJ....156..241H viz 16       D               1 311107 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.
2019ApJ...879...59Y 42           X         1 13 ~ A double-modulation effect detected in a double-mode high-amplitude δ Scuti star: KIC 10284901. YANG T.-Z. and ESAMDIN A.
2020ApJS..249...18C 17       D               1 770206 122 The Zwicky Transient Facility catalog of periodic variable stars. CHEN X., WANG S., DENG L., et al.
2021MNRAS.500.5009M viz 17       D               1 168 ~ A theoretical scenario for Galactic RR Lyrae in the Gaia data base: constraints on the parallax offset. MARCONI M., MOLINARO R., RIPEPI V., 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.
2022AJ....164...45N viz 108       D     X         3 31 2 Evaluating the V-band Photometric Metallicity with Fundamental Mode RR Lyrae in the Kepler Field. NGEOW C.-C.
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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