NGC 3201 3122 , the SIMBAD biblio

NGC 3201 3122 , the SIMBAD biblio (11 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.14CEST19:37:41


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Title First 3 Authors
1977A&AS...28..409L 66 68 UBV photometry of bright stars in NGC 3201. LEE S.W.
1984AJ.....89..231C 105 30 BV photometry of RR Lyrae variables in the globular cluster NGC 3201. CACCIARI C.
1996PAZh...22..269S viz 97 ~ The giant branch and variable stars in the globular cluster NGC 3201. SAMUS N.N., KRAVTSOV V.V., PAVLOV M.V., et al.
2014MNRAS.439.3765D viz 15       D               1 377 16 Mid-infrared period-luminosity relations for globular cluster RR Lyrae. DAMBIS A.K., RASTORGUEV A.S. and ZABOLOTSKIKH M.V.
2018ApJ...864...57M viz 297       D     X         8 159 5 Chemical compositions of field and globular cluster RR Lyrae stars. I. NGC 3201. MAGURNO D., SNEDEN C., BRAGA V.F., et al.
2019A&A...622A..60C viz 16       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.
2020AJ....160..120J viz 16       D               1 365763 238 APOGEE data and spectral analysis from SDSS Data Release 16: seven years of observations including first results from APOGEE-South. JONSSON H., HOLTZMAN J.A., ALLENDE PRIETO C., et al.
2022AJ....163..152S viz 17       D               1 583193 11 APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars. SPRAGUE D., CULHANE C., KOUNKEL M., et al.
2022ApJS..259...35A viz 17       D               1 611005 320 The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data. ABDURRO'UF, ACCETTA K., AERTS C., et al.
2022MNRAS.513..788G viz 17       D               1 690 10 New LZ and PW(Z) relations of RR Lyrae stars calibrated with Gaia EDR3 parallaxes. GAROFALO A., DELGADO H.E., SARRO L.M., et al.
2022ApJS..261...33D viz 17       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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