J/A+A/671/A61ivo://CDS.VizieR/J/A+A/671/A61doi:10.26093/cds/vizier.36710061CDSNepal 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.2023-05-16T07:17:38Z2023-03-06T08:00:37ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frsurveysstandard-starschemical-abundancesspectroscopyeffective-temperaturevisible-astronomyWith 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. 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. We trained a convolution neural network (CNN), coupling Gaia-ESO Survey iDR6 stellar labels (Teff, log(g), [Fe/H], and A(Li)) and GIRAFFE HR15N spectra, to infer the atmospheric parameters and lithium abundances for 40000 stars. The CNN architecture and accompanying notebooks are available online via GitHub at https://github.com/SamirNepal. 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{AA}, 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. 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 at 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.2023A&A...671A..61Nhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/671/A61CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/671/A61https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/671/A61http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/671/A61https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/671/A61https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/671/A61http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/671/A61GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/671/A61/table1 (Catalog of CNN result)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/671/A61/table1?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/671/A61/table1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/671/A61/table1?GETtext/xml+votable180.050000true256.0431667-37.90402780.0055555555555555565/4122 5429 5514 5723 5909 5934 9239 9282 9307 9329 9398 9838 9924 6/15 4298 4366 4376 4444 4574 4783 6148-6149 8767 8845 9216 12341 12953 13411 14336 16447 16576-16577 16603 17028 17425 18366 18907 19676 20100 20562 20763 20834 20896 20975 21017 21372 21518-21519 21528-21529 21550 21567 21594 21610 21730 22078-22079 22118 22120 22224-22225 22237 22239 22249 22300 22432-22433 22498-22499 22807 22980-22981 23096 23328 23497 23540 23547 23612 23695 23704-23706 23717 23719 23728 23730-23731 23745 23776 23820 24322 24328 24330 24344-24345 24715 24777 26302 26457 26552 26560 26583 26772 26975 27255 28548 28806-28807 28812-28813 28822-28823 28889-28891 29523 29951 30122 30215 30221 30226 30292-30293 30295 30301 30308 30464 30466-30467 30472-30473 30635 30878 31087 31293 31756 31781 31785 31787-31788 31790 31809 31812 32089 32969 33281 33321 33323 33330 33634-33635 33640-33641 34073 34317 35118 35361 36035 36693 36697 36947 36950 36953 36955 37122-37123 37225 37227 37240-37242 37278-37279 37291 37294 37313 37586-37587 37636 37785 37787-37788 37790 37809 37812 37933 37938 37944 38016 38018 38216 38441 38566 38936 39181 39183 39192 39194 39205 39216-39218 39252 39254 39257 39259-39260 39358 39672 39685 40021 41466 41649 41678 41763 41852 42081 42153 42156 42166 42177 42248 42374 42380 42394 42464 42496 42959 42980-42981 42983 42992 43034 43228 43665 44542 44921 45721 45944 47022 47125 47168 47952 48964https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/671/A61?format=asciiOpticaldefaultJ/A+A/671/A61/table1Catalog of CNN resultspNameName of the spectrummeta.idcharTeffEffective temperatureKphys.temperature.effectivefloate_TeffUncertainty of TeffKstat.error;phys.temperature.effectivefloatf_Teff[01] Boundary flag for Teff (1)meta.codecharloggSurface gravitylog(cm.s**-2)phys.gravityfloate_loggUncertainty of log(g)log(cm.s**-2)stat.errorfloatf_logg[01] Boundary flag for log(g) (1)meta.codechar[Fe/H]Metallicity [Fe/H]phys.abund.ZfloatA(Li)Lithium abundancephys.abundfloatrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintCNameGES ID (HHMMSSss+DDMMSSs)meta.id;meta.mainchare_[Fe/H]Uncertainty of [Fe/H]stat.error;phys.abund.Fefloatf_[Fe/H][0/1] Boundary flag for [Fe/H] (1)meta.codeinte_A(Li)Uncertainty of A(Li)stat.errorfloatf_A(Li)[0/1] Boundary flag for A(Li) (1)meta.codeintsnrSignal-to-noise ratiopixel**-1stat.snrdouble_RAPositions from CName (right ascension part)degpos.eq.ra;meta.mainnullableindexed_DEPositions from CName (declination part)degpos.eq.dec;meta.mainnullableindexed