2MASS J19242474+0044104 , the SIMBAD biblio

2MASS J19242474+0044104 , the SIMBAD biblio (9 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.19CEST00:12:50


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
2016A&A...589A.115S viz 16       D               1 1299 54 The Gaia-ESO Survey: Sodium and aluminium abundances in giants and dwarfs. Implications for stellar and Galactic chemical evolution. SMILJANIC R., ROMANO D., BRAGAGLIA A., et al.
2016MNRAS.461.3336C 94       D         F     6 54 75 The Gaia-ESO Survey: revisiting the Li-rich giant problem. CASEY A.R., RUCHTI G., MASSERON T., et al.
2020AJ....159....9H viz 16       D               1 86 ~ HD 150382: a lithium-rich star at the early-AGB stage? HOLANDA N., DRAKE N.A. and PEREIRA C.B.
2021A&A...651A..84M viz 17       D               2 11678 30 The Gaia-ESO survey: Mixing processes in low-mass stars traced by lithium abundance in cluster and field stars. MAGRINI L., LAGARDE N., CHARBONNEL C., et al.
2021MNRAS.506..150B viz 17       D               1 588596 276 The GALAH+ survey: Third data release. BUDER S., SHARMA S., KOS J., et al.
2021MNRAS.508.4202Z viz 17       D               1 4710 6 The GALAH+ survey: a new library of observed stellar spectra improves radial velocities and hints at motions within M67. ZWITTER T., KOS J., BUDER S., 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.

goto View the references in ADS