[TGB2011] J165900.7146-524007.747 , the SIMBAD biblio

[TGB2011] J165900.7146-524007.747 , the SIMBAD biblio (9 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.19CEST07:55:38

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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
2009A&A...505.1129M viz 15       D               1 3100 17 BVRIJHK photometry and proper motion analysis of NGC 6253 and the surrounding field. MONTALTO M., PIOTTO G., DESIDERA S., et al.
2011A&A...534A..91T viz 15       D               1 11620 2 Proper motion and densification of the international celestial reference frame in the direction of the Galactic Bulge. TEIXEIRA R., GALLI P.A.B., BENEVIDES-SOARES P., et al.
2014AJ....147..138C viz 15       D               1 450 12 Rotational and radial velocities of 1.3-2.2 M☉ red giants in open clusters. CARLBERG J.K.
2020A&A...635A.155M viz 16       D               1 812 ~ The Gaia-ESO Survey: detection and characterisation of single-line spectroscopic binaries. MERLE T., VAN DER SWAELMEN M., VAN ECK S., et al.
2021A&A...647A..19T viz 17       D               1 24814 55 3D kinematics and age distribution of the open cluster population. TARRICQ Y., SOUBIRAN C., CASAMIQUELA L., et al.
2022A&A...666A.121R viz 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 viz 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 viz 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 viz 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.

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