The Gaia-ESO Survey: Preparing the ground for 4MOST and WEAVE galactic surveys Chemical evolution of lithium with machine learning
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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.; Valentini, M.; Chiappini, C.; Steinmetz, M.; Ambrosch, M.; Pancino, E.; Jeffries, R. D.; Bensby, T.; Romano, D.; Smiljanic, R.; Dantas, M. L. L.; Gilmore, G.; Randich, S.; Bayo, A.; Bergemann, M.; Franciosini, E.; Jimenez-Esteban, F.; Jofre, P.; Morbidelli, L.; Sacco, G. G.; Tautvaisiene, G.; Zaggia, S.
I: Astronomy & Astrophysics, Bind 671, A61, 06.03.2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - The Gaia-ESO Survey
T2 - Preparing the ground for 4MOST and WEAVE galactic surveys Chemical evolution of lithium with machine learning
AU - Nepal, S.
AU - Guiglion, G.
AU - de Jong, R. S.
AU - Valentini, M.
AU - Chiappini, C.
AU - Steinmetz, M.
AU - Ambrosch, M.
AU - Pancino, E.
AU - Jeffries, R. D.
AU - Bensby, T.
AU - Romano, D.
AU - Smiljanic, R.
AU - Dantas, M. L. L.
AU - Gilmore, G.
AU - Randich, S.
AU - Bayo, A.
AU - Bergemann, M.
AU - Franciosini, E.
AU - Jimenez-Esteban, F.
AU - Jofre, P.
AU - Morbidelli, L.
AU - Sacco, G. G.
AU - Tautvaisiene, G.
AU - Zaggia, S.
PY - 2023/3/6
Y1 - 2023/3/6
N2 - Context. With its origin coming from several sources (Big Bang, stars, cosmic rays) and given its strong depletion during its stellar lifetime, the lithium element is of great interest as its chemical evolution in the Milky Way is not well understood at present. To help constrain stellar and galactic chemical evolution models, numerous and precise lithium abundances are necessary for a large range of evolutionary stages, metallicities, and Galactic volume.Aims. In the age of stellar parametrization on industrial scales, spectroscopic surveys such as APOGEE, GALAH, RAVE, and LAMOST have used data-driven methods to rapidly and precisely infer stellar labels (atmospheric parameters and abundances). To prepare the ground for future spectroscopic surveys such as 4MOST and WEAVE, we aim to apply machine learning techniques to lithium measurements and analyses.Methods. We trained a convolution neural network (CNN), coupling Gaia-ESO Survey iDR6 stellar labels (T-eff, log(g), [Fe/H], and A(Li)) and GIRAFFE HR15N spectra, to infer the atmospheric parameters and lithium abundances for similar to 40 000 stars. The CNN architecture and accompanying notebooks are available online via GitHub.Results. We show that the CNN properly learns the physics of the stellar labels, from relevant spectral features through a broad range of evolutionary stages and stellar parameters. The lithium feature at 6707.8 angstrom is successfully singled out by our CNN, among the thousands of lines in the GIRAFFE HR15N setup. Rare objects such as lithium-rich giants are found in our sample. This level of performance is achieved thanks to a meticulously built, high-quality, and homogeneous training sample.Conclusions. The CNN approach is very well adapted for the next generations of spectroscopic surveys aimed at studying (among other elements) lithium, such as the 4MIDABLE-LR/HR (4MOST Milky Way disk and bulge low- and high-resolution) surveys. In this context, the caveats of machine-learning applications should be appropriately investigated, along with the realistic label uncertainties and upper limits for abundances.
AB - Context. With its origin coming from several sources (Big Bang, stars, cosmic rays) and given its strong depletion during its stellar lifetime, the lithium element is of great interest as its chemical evolution in the Milky Way is not well understood at present. To help constrain stellar and galactic chemical evolution models, numerous and precise lithium abundances are necessary for a large range of evolutionary stages, metallicities, and Galactic volume.Aims. In the age of stellar parametrization on industrial scales, spectroscopic surveys such as APOGEE, GALAH, RAVE, and LAMOST have used data-driven methods to rapidly and precisely infer stellar labels (atmospheric parameters and abundances). To prepare the ground for future spectroscopic surveys such as 4MOST and WEAVE, we aim to apply machine learning techniques to lithium measurements and analyses.Methods. We trained a convolution neural network (CNN), coupling Gaia-ESO Survey iDR6 stellar labels (T-eff, log(g), [Fe/H], and A(Li)) and GIRAFFE HR15N spectra, to infer the atmospheric parameters and lithium abundances for similar to 40 000 stars. The CNN architecture and accompanying notebooks are available online via GitHub.Results. We show that the CNN properly learns the physics of the stellar labels, from relevant spectral features through a broad range of evolutionary stages and stellar parameters. The lithium feature at 6707.8 angstrom is successfully singled out by our CNN, among the thousands of lines in the GIRAFFE HR15N setup. Rare objects such as lithium-rich giants are found in our sample. This level of performance is achieved thanks to a meticulously built, high-quality, and homogeneous training sample.Conclusions. The CNN approach is very well adapted for the next generations of spectroscopic surveys aimed at studying (among other elements) lithium, such as the 4MIDABLE-LR/HR (4MOST Milky Way disk and bulge low- and high-resolution) surveys. In this context, the caveats of machine-learning applications should be appropriately investigated, along with the realistic label uncertainties and upper limits for abundances.
KW - techniques: spectroscopic
KW - methods: data analysis
KW - surveys
KW - stars: fundamental parameters
KW - stars: abundances
KW - Galaxy: stellar content
KW - PRE-MAIN-SEQUENCE
KW - STELLAR SPECTRA
KW - LI EVOLUTION
KW - GALAH SURVEY
KW - RED GIANTS
KW - STARS
KW - ABUNDANCES
KW - SPECTROSCOPY
KW - DWARF
KW - MILKY
U2 - 10.1051/0004-6361/202244765
DO - 10.1051/0004-6361/202244765
M3 - Journal article
VL - 671
JO - Astronomy & Astrophysics
JF - Astronomy & Astrophysics
SN - 0004-6361
M1 - A61
ER -
ID: 342498139