NGC 5272 170 , the SIMBAD biblio

NGC 5272 170 , the SIMBAD biblio (23 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.23CEST13:52:22


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Title First 3 Authors
1908AnPar..25....1V viz 1572 ~ Catalogue de 1571 etoiles contenues dans l'amas globulaire Messier 3 (NGC 5272). VON ZEIPEL M.H.
1989ApJS...71..713S 392 8 An objective-prism survey for outlying blue horizontal-branch stars in globular clusters. SANDULEAK N.
1994A&AS..104..161T viz 728 21 Proper motion study of the globular cluster M 3. TUCHOLKE H.-J., SCHOLZ R.-D. and BROSCHE P.
1994PAZh...20..693E viz 287 ~ Identifications and coordinates of variable stars in the globular cluster M 3 = NGC 5272. EVSTIGNEEVA N.M., SAMUS N.N., TSVETKOVA T.M., et al.
2001AJ....122.3183C 209 59 BV photometry of the RR Lyrae variables of the globular cluster M 3. CORWIN T.M. and CARNEY B.W.
2001AJ....122.3212S 39 12 The RR Lyrae variables in M3. I. Abundance determinations. SANDSTROM K., PILACHOWSKI C.A. and SAHA A.
2003A&A...403..587J 32 3 The metallicity range of variables in M 3. JURCSIK J.
2012MNRAS.419.2173J viz 93       D         F     2 141 30 Long-term photometric monitoring of RR Lyrae stars in Messier 3. JURCSIK J., HAJDU G., SZEIDL B., et al.
2013ApJ...763...32D viz 16       D               1 12288 202 Probing the outer galactic halo with RR Lyrae from the Catalina surveys. DRAKE A.J., CATELAN M., DJORGOVSKI S.G., et al.
2013AJ....146...21S viz 16       D               1 5702 98 Exploring the variable sky with LINEAR. II. Halo structure and substructure traced by RR Lyrae stars to 30 kpc. SESAR B., IVEZIC Z., STUART J.S., et al.
2013AJ....146..101P viz 16       D               1 7198 120 Exploring the variable sky with LINEAR. III. Classification of periodic light curves. PALAVERSA L., IVEZIC Z., EYER L., et al.
2014MNRAS.439.3765D viz 16       D               1 377 16 Mid-infrared period-luminosity relations for globular cluster RR Lyrae. DAMBIS A.K., RASTORGUEV A.S. and ZABOLOTSKIKH M.V.
2014MNRAS.445.1584S viz 16       D               1 556 7 Characteristics of bright AB-type RR Lyrae stars from the ASAS and WASP surveys. SKARKA M.
2015A&A...573A.100F viz 16       D               1 335 8 The WFCAM multiwavelength Variable Star Catalog. FERREIRA LOPES C.E., DEKANY I., CATELAN M., et al.
2015AJ....150..107Y viz 16       D               2 1171 5 Photometry of variable stars from the THU-NAOC transient survey. I. The first two years. YAO X., WANG L., WANG X., 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.
2017MNRAS.468.1317J 97       D         F     5 195 21 Photometric and radial-velocity time series of RR Lyrae stars in M3: analysis of single-mode variables. JURCSIK J., SMITOLA P., HAJDU G., et al.
2018ApJ...857...55H 97 10 A data-driven study of RR Lyrae Near-IR light curves: principal component analysis, robust fits, and metallicity estimates. HAJDU G., DEKANY I., CATELAN M., 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.
2020AJ....160..220B viz 17       D               1 240 ~ Near-infrared census of RR Lyrae variables in the Messier 3 globular cluster and the period-luminosity relations. BHARDWAJ A., REJKUBA M., DE GRIJS 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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