V* MQ Vul , the SIMBAD biblio

V* MQ Vul , the SIMBAD biblio (11 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.05.16CEST03:29:20


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Bibcode/DOI Score in Title|Abstract|
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in a table in teXt, Caption, ... Nb occurence Nb objects in ref Citations
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Title First 3 Authors
1972PZ.....18..497K 19 2 The investigation of 18 variables stars in the area M 56. KUROCHKIN N.E.
1975IBVS..961....1K 498 5 60th name-list of variable stars. KUKARKIN B.V., KHOLOPOV P.N., KUKARKINA N.P., et al.
1985PZ.....22..201K 25 1 New data on the variable stars in the region of M 56. KUROCHKIN N.E.
1999IBVS.4723....1S 90 0 Coordinates and identifications for Kurochkin's variables near M 56. SKIFF B.A.
2014AJ....147..119C viz 16       D               1 8010 91 Contamination in the Kepler field. Identification of 685 KOIs as false positives via ephemeris matching based on Q1-Q12 data. COUGHLIN J.L., THOMPSON S.E., BRYSON S.T., 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.
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 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.
2020ApJS..249...18C 17       D               1 777630 122 The Zwicky Transient Facility catalog of periodic variable stars. CHEN X., WANG S., DENG L., 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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