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| V* V561 Her , the SIMBAD biblio (9 results) | C.D.S. - SIMBAD4 rel 1.8 - 2024.04.19CEST12:46:43 |
Sort references on where and how often the object is cited
trying to find the most relevant references on this object.
More on score
| 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 |
|---|---|---|---|---|---|---|---|---|---|
| 1966AN....289....1H | 596 | 25 | Mitteilungen uber neuentdeckte veraenderliche Sterne. | HOFFMEISTER C. | |||||
| 1968IBVS..311....1K | 1646 | 13 | Identification list of the new variable stars nominated in 1968 (an extract from the 56th nominating list) | KUKARKIN B.V., KHOLOPOV P.N., EFREMOV Y.N., et al. | |||||
| 1986AJ.....91..354Y | 661 | 25 | On optical studies of high-velocity clouds. | YORK D.G., BURKS G.S. and GIBNEY T.B. | |||||
2014MNRAS.441..715G 
|
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. | ||
|
2014ApJS..213....9D
|
16 | D | 1 | 46794 | 385 | The Catalina surveys periodic variable star catalog. | DRAKE A.J., GRAHAM M.J., DJORGOVSKI S.G., 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. | ||
|
2018AJ....156..241H
|
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
|
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. | ||
|
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. |
