CoRoT 100576007 , the SIMBAD biblio

CoRoT 100576007 , the SIMBAD biblio (11 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.17CEST01:37:06


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Title First 3 Authors
2013A&A...550A.120S viz 15       D               1 6832 13 Improved variability classification of CoRoT targets with Giraffe spectra. SARRO L.M., DEBOSSCHER J., NEINER C., et al.
2015A&A...580A..21T viz 16       D               1 284 9 BEER analysis of Kepler and CoRoT light curves. III. Spectroscopic confirmation of seventy new beaming binaries discovered in CoRoT light curves. TAL-OR L., FAIGLER S. and MAZEH T.
2019ApJ...879...69T viz 16       D               1 222610 141 The Payne: self-consistent ab initio fitting of stellar spectra. TING Y.-S., CONROY C., RIX H.-W., et al.
2020ApJ...900....4S viz 16       D               1 121537 14 The age distribution of stars in the Milky Way bulge. SIT T. and NESS M.K.
2021MNRAS.506..150B viz 17       D               1 588596 276 The GALAH+ survey: Third data release. BUDER S., SHARMA S., KOS J., et al.
2022AJ....163..152S viz 17       D               1 583193 11 APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars. SPRAGUE D., CULHANE C., KOUNKEL M., et al.
2022ApJS..259...35A viz 17       D               1 611005 320 The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data. ABDURRO'UF, ACCETTA K., AERTS C., et al.
2022ApJ...929...33H viz 17       D               1 114072 2 Quantifying Chemical and Kinematical Properties of Galactic Disks. HU G. and SHAO Z.
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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