V* NS UMa , the SIMBAD biblio

V* NS UMa , the SIMBAD biblio (11 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.20CEST00:05:06

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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
2006AJ....132.1202K viz 15       D               1185 88 Analysis of RR Lyrae stars in the northern sky variability survey. KINEMUCHI K., SMITH H.A., WOZNIAK P.R., et al.
2006MNRAS.368.1757W viz 617 72 A catalogue of RR Lyrae stars from the Northern Sky Variability Survey. WILS P., LLOYD C. and BERNHARD K.
2006IBVS.5699....1.         O           104 0 Observations of variables.
2008IBVS.5863....1K viz 15       D               2 1274 25 The 79th name-list of variable stars. KAZAROVETS E.V., SAMUS N.N., DURLEVICH O.V., et al.
2012AJ....144...39L viz 93       D       C       2 144 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.
2013A&A...549A.101S viz 55       D     X         2 242 26 Known Galactic field Blazhko stars. SKARKA M.
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.
2017A&A...605A..79G viz 16       D               1 735 92 Gaia Data Release 1. Testing parallaxes with local Cepheids and RR Lyrae stars. GAIA COLLABORATION, CLEMENTINI G., EYER 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.
2019MNRAS.490.3158C viz 17       D               1 465834 28 A catalogue of stellar diameters and fluxes for mid-infrared interferometry. CRUZALEBES P., PETROV R.G., ROBBE-DUBOIS S., 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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