| Title | The Gaia -ESO survey: chemical evolution of Mg and Al in the Milky Way with machine learning / |
|---|---|
| Authors | Ambrosch, Markus ; Guiglion, G ; Mikolaitis, Šarūnas ; Chiappini, C ; Tautvaišienė, Gražina ; Nepal, S ; Gilmore, G ; Randich, S ; Bensby, T ; Bayo, A ; Bergemann, M ; Morbidelli, L ; Pancino, E ; Sacco, G. G ; Smiljanic, R ; Zaggia, S ; Jofré, P ; Jiménez-Esteban, F. M |
| DOI | 10.1051/0004-6361/202244766 |
| Full Text |
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| Is Part of | Astronomy & astrophysics.. Les Ulis : EDP Sciences. 2023, vol. 672, art. no. A46, p. [1-17].. ISSN 0004-6361. eISSN 1432-0746 |
| Keywords [eng] | galaxy: abundances ; galaxy: stellar content ; methods: data analysis ; stars: abundances ; techniques: spectroscopic |
| Abstract [eng] | Context. To take full advantage of upcoming large-scale spectroscopic surveys, it will be necessary to parameterize millions of stellar spectra in an efficient way. Machine learning methods, especially convolutional neural networks (CNNs), will be among the main tools geared at achieving this task. |
| Published | Les Ulis : EDP Sciences |
| Type | Journal article |
| Language | English |
| Publication date | 2023 |
| CC license |
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