J/A+A/692/A228ivo://CDS.VizieR/J/A+A/692/A228doi:10.26093/cds/vizier.36920228CDSCandebat N.Sacco G.G.Magrini L.Belfiore F.Van der Swaelmen M.,Zibetti S.2025-02-26T20:02:34Z2024-12-16T09:00:13ZCDS support team

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

cds-question@unistra.frsurveyschemical-abundanceseffective-temperaturevisible-astronomyradial-velocityNew spectroscopic surveys will increase the number of astronomical objects in need of characterisation by more than an order of magnitude. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms cannot directly estimate error, making them unsuitable for reliable science.We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra. This algorithm accurately infers precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra.We trained a conditional invertible neural network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR10 and HR21 setups) within the Gaia-ESO survey. A key feature of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum. By analysing this distribution, we inferred stellar parameters and their corresponding uncertainties. We carried out several tests to investigate how parameters are inferred and errors are estimated.We achieved an accuracy of 28K in Teff, 0.06dex in logg, 0.03dex in [Fe/H], and between 0.05dex and 0.17dex for the other abundances for high-quality spectra. Accuracy remains stable with low signal-to-noise ratio (between 5 and 25) spectra, with an accuracy of 39K in Teff, 0.08dex in logg, and 0.05dex in [Fe/H]. The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN.This neural network represents a compelling proposition for future astronomical surveys. These derived uncertainties are coherent and can therefore be reused in future works as Bayesian priors.2024A&A...692A.228Chttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/692/A228CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/A+A/676/A129 : Gaia-ESO catalogue version 5.1 (Hourihane+, 2023)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/692/A228https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/692/A228http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/692/A228https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/692/A228https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/692/A228http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/692/A228GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/692/A228/catalog (GES 5.1 for HR10 & HR21 by 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46128 46143-46144 46149 46151 46160 46162 46164-46165 46168 46179 46182 46203 46206 46229 46232 46243 46246 46263 46274 46284-46285 46295 46306 46309 46315 46320 46343 46354 46357 46363 46366 46368-46369 46377 46380 46418-46419 46433 46436 46449 46466 46494 46516 46529 46532 46608-46609 46617 46636-46637 46657 46659 46685 46687 46733 46735 47105-47106 47108 47112 47125 47168 47232 47274 47319 47344 47388 47390 47405 47407 47416 47418 47425 47490 47644 47646-47647 47655 47660-47661 47668-47670 47678-47679 47683 47686 47689-47690 47692 47712 47716 47720 47725 47736 47747 47753-47754 47756 47758-47759 47763-47765 47769 47776 47780-47781 47786 47809 47812 47847 47952 48501 48509-48511 48596-48598 48607 48612 48767 48803 48856 48858 48911 48964 49141 49149 49151https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/692/A228?format=asciiOpticaldefaultJ/A+A/692/A228/catalogGES 5.1 for HR10 & HR21 by OssicoNNe1_Ca1NNInternal error on CA1NNstat.errorfloate2_Ca1NNExternal error on CA1NNstat.errordoubleNi1NNNeutral Nickel abundance (2)phys.abundfloate_Ni1NNError on NI1NNstat.errordoublee1_Ni1NNInternal error on NI1NNstat.errorfloate1_Ti1NNInternal error on TI1NNstat.errordoublee2_Ti1NNExternal error on TI1NNstat.errordoubleSi1NNNeutral Silicon abundance (2)phys.abunddoublee_Si1NNError on SI1NNstat.errordoublee1_Si1NNInternal error on SI1NNstat.errordoublee2_Si1NNExternal error on SI1NNstat.errordouble_RARight Ascension (J2000) from SIMBAD (not part of the original data)degpos.eq.ra;meta.mainindexed_DEDeclination (J2000) from SIMBAD (not part of the original data)degpos.eq.dec;meta.mainindexedObjectGES object name from coordinatesmeta.id;meta.maincharRecWGWorking group deriving the recommended parametersmeta.codecharrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintSetupGrating setups used for analysisinstr.setupchare2_Ni1NNExternal error on NI1NNstat.errordoubleTi1NNNeutral Titanium abundance (2)phys.abunddoublee_Ti1NNError on TI1NNstat.errordoubleSimbadSimbad column added by the CDSmeta.ref.urlcharGESFieldGES field name from CASUobs.fieldcharMEM3D? Membership probabilitystat.probabilitydoublenullableSNRSNR of the spectrum used for deriving radial velocities (1)stat.snr;instr.setupdoubleRV? Radial Velocity (1)km/sphys.veloc;pos.heliocentricdoublenullableTeffEffective Temperature (1)Kphys.temperature.effectivedoublee_TeffError on Teff (1)Kstat.error;phys.temperature.effectivedoubleloggLog Surface Gravity (1)log(cm.s**-2)phys.gravityfloate_loggError on logg (1)log(cm.s**-2)stat.errorfloat[Fe/H]? Metallicity (1)phys.abund.Zfloatnullablee_[Fe/H]? Error on [Fe/H] (1)stat.error;phys.abund.FefloatnullableAl1? Neutral Aluminium abundance (1)phys.abundfloatnullablee_Al1? Error on AL1stat.errorfloatnullableMg1? Neutral Magnesium abundance (1)phys.abundfloatnullablee_Mg1? Error on MG1stat.errorfloatnullableCa1? Neutral Calcium abundance (1)phys.abundfloatnullablee_Ca1? Error on CA1stat.errorfloatnullableTi1? Neutral Titanium abundance (1)phys.abundfloatnullablee_Ti1? Error on TI1stat.errorfloatnullableSi1? Neutral Silicon abundance (1)phys.abundfloatnullablee_Si1? Error on SI1stat.errorfloatnullableNi1? Neutral Nickel abundance (1)phys.abundfloatnullablee_Ni1? Error on NI1stat.errorfloatnullableDatasetIn which dataset the star was usedmeta.id;meta.datasetcharTeffNNEffective Temperature (2)Kphys.temperature.effectiveinte_TeffNNError on Teff (2)Kstat.errorinte1_TeffNNInternal error on Teff (2)Kstat.errorinte2_TeffNNExternal error on Teff (2)Kstat.errorintloggNNLog Surface Gravity (2)log(cm.s**-2)phys.gravityfloate_loggNNError on logg (2)log(cm.s**-2)stat.errorfloate1_loggNNInternal error on logg (2)log(cm.s**-2)stat.errorfloate2_loggNNExternal error on logg (2)log(cm.s**-2)stat.errorfloat[Fe/H]NNMetallicity (2)phys.abund.Fedoublee_[Fe/H]NNError on [Fe/H] (2)stat.error;phys.abund.Fedoublee1_[Fe/H]NNInternal error on [Fe/H] (2)stat.errorfloate2_[Fe/H]NNExternal error on [Fe/H] (2)stat.errordoubleAl1NNNeutral Aluminium abundance (2)phys.abundfloate_Al1NNError on AL1NNstat.errorfloate1_Al1NNInternal error on AL1NNstat.errorfloate2_Al1NNExternal error on AL1NNstat.errorfloatMg1NNNeutral Magnesium abundance (2)phys.abunddoublee_Mg1NNError on MG1NNstat.errordoublee1_Mg1NNInternal error on MG1NNstat.errordoublee2_Mg1NNExternal error on MG1NNstat.errordoubleCa1NNNeutral Calcium abundance (2)phys.abundfloate_Ca1NNError on CA1NNstat.errordouble