J/A+A/673/A105ivo://CDS.VizieR/J/A+A/673/A105doi:10.26093/cds/vizier.36730105CDSBello-Garcia A.Passegger V.M.Ordieres-Mere J.Schweitzer A.,Caballero J.A.Gonzalez-Marcos A.Ribas I.Reiners A.Quirrenbach A.,Amado P.J.Bejar V.J.S.Cifuentes C.Henning T.Kaminski A.Luque R.,Montes D.Morales J.C.Pedraz S.Tabernero H.M.Zechmeister M.2024-11-06T20:03:52Z2023-05-16T07:45:51ZCDS support team

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

cds-question@unistra.frm-starsmetallicityeffective-temperaturevisible-astronomyThe large amounts of astrophysical data being provided by existing and future instrumentation require efficient and fast analysis tools. Transfer learning is a new technique promising higher accuracy in the derived data products, with information from one domain being transferred to improve the accuracy of a neural network model in another domain. In this work, we demonstrate the feasibility of applying the deep transfer learning (DTL) approach to high-resolution spectra in the framework of photospheric stellar parameter determination. To this end, we used 14 stars of the CARMENES survey sample with interferometric angular diameters to calculate the effective temperature, as well as six M dwarfs that are common proper motion companions to FGK-type primaries with known metallicity. After training a deep learning (DL) neural network model on synthetic PHOENIX-ACES spectra, we used the internal feature representations together with those 14+6 stars with independent parameter measurements as a new input for the transfer process. We compare the derived stellar parameters of a small sample of M dwarfs kept out of the training phase with results from other methods in the literature. Assuming that temperatures from bolometric luminosities and interferometric radii and metallicities from FGK+M binaries are sufficiently accurate, DTL provides a higher accuracy than our previous state-of-the-art DL method (mean absolute differences improve by 20K for temperature and 0.2dex for metallicity from DL to DTL when compared with reference values from interferometry and FGK+M binaries). Furthermore, the machine learning (internal) precision of DTL also improves as uncertainties are five times smaller on average. These results indicate that DTL is a robust tool for obtaining M-dwarf stellar parameters comparable to those obtained from independent estimations for well-known stars.2023A&A...673A.105Bhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/673/A105CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/A+A/577/A128 : CARMENES input catalog of M dwarfs I (Alonso-Floriano+, 2015)J/A+A/612/A49 : 324 CARMENES M dwarfs velocities (Reiners+, 2018)J/A+A/615/A6 : Photospheric parameters of CARMENES stars (Passegger+, 2018)J/A+A/621/A126 : CARMENES input catalogue of M dwarfs. IV. (Diez Alonso+ 2019)J/A+A/625/A68 : Radii and masses of the CARMENES targets (Schweitzer+, 2019)J/A+A/642/A22 : DL fundamental parameters of target stars (Passegger+, 2020)J/A+A/658/A194 : Metallicities in M dwarfs (Passegger+, 2022)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/673/A105https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/673/A105http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/673/A105https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/673/A105https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/673/A105http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/673/A105GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/673/A105/tablea1 (Teff and [M/H] values for CARMENES stars estimated by the DTL method)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/673/A105/tablea1?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/673/A105/tablea1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/673/A105/tablea1?GETtext/xml+votable180.050000true1.295371745.78656690.0055555555555555566/22 131 497 546 624 725 778 811 1033 1672 1822 1875 1938 2515 2545 2681 2737 2747 3031 3266 3345 3374 3658 3682 3715 3791 4033 4366 4444 4518 4595 5009 5041 5207 5241 5343 5420 5488 5534 5590 5849 6384 6552 6672 6676 6894 6980 7003 7103 7181 7262 7264 7395 7428 7471 7611 7775 7837 7921 7937 7940 7980 8023 8032 8123 8163 8208 8240 8394 8562 8627 8733 8843 8915 9188 9529 9578 9597 10037 10149 10225 10278 10448 10464-10465 10694-10695 10901 10999 11177 11255 11403 11636 11671 11962 12140 12184 12191 12621 12732 12865 13085 13427 13451 13567 13688 13875 14277 14395 14515 14709 14781 14850 14985 14993-14994 15052 15078 15311 15346 15431 15548 15598 15602 15635 15763 15792 15960 16041 16754 16758 17020 17271 17302 17348 17358 17412 17870 18195 18496 18553 18666 18789 18942 19050 19064 19131 19306 19352 19466 19516 19879 20023 20100 20109 20311 20459 20795 20896 21151 21171 21427 21747-21748 21953 21969 22014 22222 22277 22309 22626 22645 22709 22807 22904 22942 23014 23094 23363 23453 23564 23718 23731 23735 23743 23867 23910 23997 24213 24227 24239 24995 25009 25363 25388 25512 25528 25849 25989 26327 26487 26538 26560 26563 26583 26731 26757 26772 26821 26972 27201 27289 27320 27392 27459 27730 27734 27787 27820 27912 28223 28341 28343 28548 28646 28955 29146 29170 29684 30345 30429 30455 30490 30505 30771 30833 30893 31014 31019 31191 31229 31642 31686 31767 31781 31803 31811 32033 32076 32093 32115 32335 32392 32458 32491 32753 36732 40250 40359 40386 40436 40505 40513 40610 44518 44541 44728 44921 44936-44937 44967 48485 48500 48906 48935 48958 49101https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/673/A105?format=asciiOpticaldefaultJ/A+A/673/A105/tablea1Teff and [M/H] values for CARMENES stars estimated by the DTL methodSimbadNameSimbad column added by the CDSmeta.idcharnullablerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintKarmnCARMENES identifier (JHHMMm+DDdA)meta.id;meta.maincharNotesAdditional note (1)meta.notecharTeffEffective temperatureKphys.temperature.effectiveinte_TeffDelta Teff for -1 sigmaKstat.error;phys.temperature.effectiveintE_TeffDelta Teff for +1 sigmaKstat.error;stat.maxint[M/H]Metallicityphys.abund.Zfloate_[M/H]? Delta Metallicity for -1 sigmastat.error;phys.abundfloatnullableE_[M/H]? Delta Metallicity for +1 sigmastat.error;stat.maxfloatnullable_RARight Ascension (J2000) from SIMBAD (not part of the original data)degpos.eq.ra;meta.main_DEDeclination (J2000) from SIMBAD (not part of the original data)degpos.eq.dec;meta.main