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| NGC 6705 1714 , the SIMBAD biblio (9 results) | C.D.S. - SIMBAD4 rel 1.8 - 2026.10.16CEST01:28:04 |
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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 |
|---|---|---|---|---|---|---|---|---|---|
| 1977A&AS...27..117M | 1894 | 38 | Membership in the open cluster M 11. | McNAMARA B.J., PRATT N.M. and SANDERS W.L. | |||||
2014A&A...569A..17C 
|
15 | D | 1 | 1042 | 54 | The Gaia-ESO Survey: Stellar content and elemental abundances in the massive cluster NGC 6705. | CANTAT-GAUDIN T., VALLENARI A., ZAGGIA S., et al. | ||
|
2020MNRAS.496.4701J
|
16 | D | 1 | 23794 | 26 | The Gaia-ESO Survey: membership probabilities for stars in 32 open clusters from 3D kinematics. | JACKSON R.J., JEFFRIES R.D., WRIGHT N.J., et al. | ||
|
2020A&A...643A..71G
|
16 | D | 1 | 12328 | 25 | The Gaia-ESO Survey: Calibrating the lithium-age relation with open clusters and associations. I. Cluster age range and initial membership selections. | GUTIERREZ ALBARRAN M.L., MONTES D., GOMEZ GARRIDO M., et al. | ||
|
2022MNRAS.517..356K
|
128 | X F | 2 | 10 | 5 | Detection of 2460 double-lined spectroscopic binary candidates in the LAMOST-MRS using projected rotational velocities and a binary spectral model. | KOVALEV M., CHEN X. and HAN Z. | ||
|
2022A&A...666A.121R
|
17 | D | 1 | 38011 | 44 | The Gaia-ESO Public Spectroscopic Survey: Implementation, data products, open cluster survey, science, and legacy,. | RANDICH S., GILMORE G., MAGRINI L., et al. | ||
|
2023A&A...671A..61N
|
17 | D | 1 | 39409 | 4 | The Gaia-ESO Survey: Preparing the ground for 4MOST and WEAVE galactic surveys Chemical evolution of lithium with machine learning. | NEPAL S., GUIGLION G., DE JONG R.S., et al. | ||
|
2023A&A...676A.129H
|
17 | D | 1 | 114943 | ~ | The Gaia-ESO Survey: Homogenisation of stellar parameters and elemental abundances. | HOURIHANE A., FRANCOIS P., WORLEY C.C., et al. | ||
|
2024A&A...692A.228C
|
18 | D | 1 | 49864 | ~ | Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks. | CANDEBAT N., SACCO G.G., MAGRINI L., et al. |
