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V* OU Boo , the SIMBAD biblio (10 results) | C.D.S. - SIMBAD4 rel 1.8 - 2024.03.29CET15:10:14 |
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 | 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. | |||
2006PZP.....6....8K | 24 | 0 | New RR Lyrae variables. | KHRUSLOV A.V. | |||||
2013ApJ...763...32D | 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 | 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 | 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. | ||
2017AJ....153..204S | 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.474.4112M | 16 | D | 1 | 529 | 63 | Fourteen candidate RR Lyrae star streams in the inner Galaxy. | MATEU C., READ J.I. and KAWATA D. | ||
2019A&A...622A..60C | 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. | ||
2021ApJ...912..144M | 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 | 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. |