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| Cl* NGC 362 ALC 36 , the SIMBAD biblio (9 results) | C.D.S. - SIMBAD4 rel 1.8 - 2026.10.15CEST20:05:45 |
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 |
|---|---|---|---|---|---|---|---|---|---|
1992A&AS...93..311T 
|
546 | 20 | Astrometry of globular clusters. II. Relative proper motions and the space motion of NGC 362. | TUCHOLKE H.-J. | |||||
|
2017A&A...601A.112P
|
16 | D | 1 | 542 | 90 | The Gaia-ESO Survey. Mg-Al anti-correlation in iDR4 globular clusters. | PANCINO E., ROMANO D., TANG B., et al. | ||
|
2018MNRAS.478.1520B
|
16 | D | 1 | 11619 | 300 | A catalogue of masses, structural parameters, and velocity dispersion profiles of 112 Milky Way globular clusters. | BAUMGARDT H. and HILKER M. | ||
|
2019A&A...628A..54K
|
16 | D | 1 | 734 | 40 | Non-LTE chemical abundances in Galactic open and globular clusters. | KOVALEV M., BERGEMANN M., TING Y.-S., et al. | ||
|
2022MNRAS.509.1664J
|
17 | D | 1 | 14101 | 25 | The Gaia-ESO Survey: Membership probabilities for stars in 63 open and 7 globular clusters from 3D kinematics. | JACKSON R.J., JEFFRIES R.D., WRIGHT N.J., et al. | ||
|
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. |
