J/ApJ/562/528ivo://CDS.VizieR/J/ApJ/562/528doi:10.26093/cds/vizier.15620528CDSSnider S.Allende Prieto C.von Hippel T.Beers T.C.Sneden C.,Qu Y.Rossi S.2002-03-11T17:55:48Z2002-03-11T18:54:07ZCDS support team

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

cds-question@unistra.frpopulation-ii-starsspectroscopyeffective-temperatureWe explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (T_eff_, log(g), and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution ({Delta}{lambda}~1-2{AA}) nonflux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of metallicity and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict T_eff_ with an accuracy of {sigma}(T_eff_)=135-150K over the range 4250K<=T_eff_<=6500K, logg with an accuracy of {sigma}(logg)=0.25-0.30dex over the range 1.0<=logg<=5.0, and [Fe/H] with an accuracy {sigma}([Fe/H])=0.15-0.20dex over the range -4.0<=[Fe/H]<=0.3. Such accuracies are competitive with the results obtained by fine analysis of high-resolution spectra. It is noteworthy that the ANNs are able to obtain these results without consideration of photometric information for these stars. We have also explored the impact of the signal-to-noise ratio (S/N) on the behavior of ANNs and conclude that, when analyzed with ANNs trained on spectra of commensurate S/N, it is possible to extract physical parameter estimates of similar accuracy with stellar spectra having S/N as low as 13. Taken together, these results indicate that the ANN approach should be of primary importance for use in present and future large-scale spectroscopic surveys. The stars that comprise our study are a subset of the calibration stars used in the Beers et al. (1999, Cat. <J/AJ/117/981>) medium-resolution surveys.2001ApJ...562..528Shttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/562/528CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/AJ/117/981 : Estimation of stellar metal abundance. II. (Beers+, 1999)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/562/528https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/562/528http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/562/528https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/562/528https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/562/528http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJ/562/528GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/ApJ/562/528/table2 (Catalog and ANN parameters for the training sample)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJ/562/528/table2?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJ/562/528/table2?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJ/562/528/table2?GETtext/xml+votable180.050000true202.4829652.7574380.005555555555555556Cone search capability for table J/ApJ/562/528/table3 (Catalog and ANN parameters for the testing sample)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJ/562/528/table3?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJ/562/528/table3?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJ/562/528/table3?GETtext/xml+votable180.050000true215.438590.7831040.0055555555555555566/94 154 208 373 798 2736 2921 3712 4129 4376 4423 4429 4694 4714 5166 5195 5258 5458 5555 6036 6058 6256 6348 6792 7424 7613 7710 7767 8210 8216 8255 8296 8371 8377 9004 9065 9121 9992 10458 10476 11197 11412 12451 12621 12645 12650 12704 12844 13020 13075 14534 14693 14743 15176 17099 17124 17136 17171 17234 17245 17343 17369 17538 17564 17578 18425 18553 18561 18707 18719 18735 18936 19244 19603 19973 20390 21269 21299 21485 21809 21866 22071 22130 22162 22186 22193 22205 22401 22420 22451 22743 22748 22998 23011 23013 23015 23070 23096 23190 23428 23567 23978 24752 24942 25051 25331 25385 25554 25567 25573 25591 25595 25790 25834 25838 25846 25854 25891 25906 25963 25966 26025 26030 26031 26109 26187 26239 26284 26289 26292 26305 26344 26347 26366 26380 26457 26528 26534 26576 26728 26852 26989 27040 27073 27075 27162 27163 27224 27239 27255 27323 27330 27380 27394 27456 27503 27592 27661 27763 27777 27796 27920 27948 27975 28154 28181 28258 28336 28338 28342 28396 28421 28459 28664 29490 29548 29593 29640 29769 29915 30051 30131 30166 30167 30296 30359 30464 30489 30528 30577 30831 30939 30965 30974 31094 31100 31132 31145 31306 31347 31366 31393 31397 31416 31472 31501 31503 31585 31650 31677 31748 31826 32034 32302 32352 32468 32593 33566 33642 33831 35647 35768 35948 36084 36365 36700 36745 36813 36888 37125 37232 38255 38868 39647 39692 40222 40337 40567 40716 40831 40925 40931 40944 41259 42331 42496 43445 43546 43613 43786 43788 43962 44121 44208 44281 44542 44771 44816 44940 45694 45790 45796 45808 45818 46023 46506 46661 47344 47347 48501 48719 48736 48798 48824 48946 49009 49063 49078https://cdsarc.cds.unistra.fr/viz-bin/moc/J/ApJ/562/528?format=asciiOpticaldefaultJ/ApJ/562/528/table2Catalog and ANN parameters for the training samplerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNameMetal-poor star namemeta.id;meta.maincharSourceSpectrum source (1)meta.id;instr.telcharf_Source[*] Indicates star is a member of the `nearby' subsamplemeta.code.errorcharCTeffThe catalog effective temperatureKphys.temperature.effectiveintf_CTeff[:] Indicates a large discrepancy with ATeffmeta.code.errorcharATeffThe artificial neural network effective temperatureKphys.temperature.effectiveintf_ATeff[:] Indicates a large discrepancy with CTeffmeta.code.errorcharClog(g)Log of the catalog surface gravitylog(cm.s**-2)phys.gravityfloatf_Clog(g)[:] Indicates a large discrepancy with Alog(g)meta.code.errorcharAlog(g)Log of the artificial neural network surface gravitylog(cm.s**-2)phys.gravityfloatf_Alog(g)[:] Indicates a large discrepancy with Clog(g)meta.code.errorcharCFe/HCatalog [Fe/H] (2)phys.abund.Fefloatf_CFe/H[:] Indicates a large discrepancy with AFe/Hmeta.code.errorcharAFe/HArtificial neural network [Fe/H] (2)phys.abund.Fefloatf_AFe/H[:] Indicates a large discrepancy with CFe/Hmeta.code.errorchar_RAPosition, taken from Simbad (right ascension part)degpos.eq.ra;meta.main_DEPosition, taken from Simbad (declination part)degpos.eq.dec;meta.mainJ/ApJ/562/528/table3Catalog and ANN parameters for the testing samplerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNameMetal-poor star namemeta.id;meta.maincharSourceSpectrum source (1)meta.id;instr.telcharf_Source[*] Indicates star is a member of the `nearby' subsamplemeta.code.errorcharCTeffThe catalog effective temperatureKphys.temperature.effectiveintf_CTeff[:] Indicates a large discrepancy with ATeffmeta.code.errorcharATeffThe artificial neural network effective temperatureKphys.temperature.effectiveintf_ATeff[:] Indicates a large discrepancy with CTeffmeta.code.errorcharClog(g)Log of the catalog surface gravitylog(cm.s**-2)phys.gravityfloatf_Clog(g)[:] Indicates a large discrepancy with Alog(g)meta.code.errorcharAlog(g)Log of the artificial neural network surface gravitylog(cm.s**-2)phys.gravityfloatf_Alog(g)[:] Indicates a large discrepancy with Clog(g)meta.code.errorcharCFe/HCatalog [Fe/H] (2)phys.abund.Fefloatf_CFe/H[:] Indicates a large discrepancy with AFe/Hmeta.code.errorcharAFe/HArtificial neural network [Fe/H] (2)phys.abund.Fefloatf_AFe/H[:] Indicates a large discrepancy with CFe/Hmeta.code.errorchar_RAPosition, taken from Simbad (right ascension part)degpos.eq.ra;meta.main_DEPosition, taken from Simbad (declination part)degpos.eq.dec;meta.main