J/ApJS/246/9ivo://CDS.VizieR/J/ApJS/246/9doi:10.26093/cds/vizier.22460009CDSZhang BoLiu C.Deng L.-C.2022-10-26T11:12:42Z2022-03-31T14:31:03ZCDS support team

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

cds-question@unistra.frchemical-abundancesvisible-astronomyspectroscopyastronomical-modelsgiant-starssurveysThe LAMOST survey has provided 9 million spectra in its Data Release 5 (DR5) at R~1800. Extracting precise stellar labels is crucial for such a large sample. In this paper, we report the implementation of the Stellar LAbel Machine (SLAM), which is a data-driven method based on support vector regression (SVR), a robust nonlinear regression technique. Thanks to the capability to model highly nonlinear problems with SVR, SLAM can generally derive stellar labels over a wide range of spectral types. This gives it a unique capability compared to other popular data-driven methods. To illustrate this capability, we test the performance of SLAM on stars ranging from Teff~4000 to ~8000K trained on LAMOST spectra and stellar labels. At g-band signal-to-noise ratio (S/Ng) higher than 100, the random uncertainties of Teff, logg, and [Fe/H] are 50K, 0.09dex, and 0.07dex, respectively. We then set up another SLAM model trained by APOGEE and LAMOST common stars to demonstrate its capability of dealing with high dimensional problems. The spectra are from LAMOST DR5 and the stellar labels of the training set are from APOGEE DR15, including Teff, logg, [M/H], [{alpha}/M], [C/M], and [N/M]. The cross-validated scatters at S/N_g_~100 are 49K, 0.10dex, 0.037dex, 0.026dex, 0.058dex, and 0.106dex for these parameters, respectively. This performance is at the same level as other up-to-date data-driven models.2020ApJS..246....9Zhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/246/9CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toV/153 : LAMOST DR4 catalogs (Luo+, 2018)V/164 : LAMOST DR5 catalogs (Luo+, 2019)J/other/RAA/11.924 : Atmospheric parameters for 771 stars (Wu+, 2011)J/ApJ/808/16 : Cannon: a new approach to determine abundances (Ness+, 2015)J/AJ/151/144 : ASPCAP weights for APOGEE chemical elements (Garcia+, 2016)J/ApJ/836/5 : Abundances of LAMOST giants from APOGEE DR12 (Ho+, 2017)J/MNRAS/478/4513 : GALAH Survey DR2 (Buder+, 2018)J/ApJS/241/32 : OB stars from the LAMOST DR5 spectra (Liu+, 2019)J/ApJS/244/8 : M-type stars in LAMOST DR5 (Zhong+, 2019)http://www.sdss.org/ : SDSS home pagehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJS/246/9https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJS/246/9http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJS/246/9https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJS/246/9https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJS/246/9http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJS/246/9GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/ApJS/246/9/catalog (The Catalog of the 5 million converged LAMOST DR5 K giant stars with SLAM-derived stellar labels)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJS/246/9/catalog?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJS/246/9/catalog?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJS/246/9/catalog?GETtext/xml+votable180.050000true332.368745-1.9557710.0055555555555555561/23 2/1 4 6 18-20 22 24 26 33-35 40 85 105 109 3/0-2 9-10 12-14 20-21 23 28-29 33-35 37-42 48 50 65-68 71 84-85 92 94 101-103 108-110 112-113 128-130 144-146 150-151 153-154 157 164 166-168 171 173-174 209 212-213 226 232-234 277 287 295 297 299-300 303-304 311 317-319 345-348 350-351 357 361 363 366-367 406-407 413 415 425-426 428 430-432 434-435 442 445 447 485 487 489 494 496 498-499 504-505 4/13-15 44-45 47 60-62 89-91 120-122 124 128-129 131 144-145 147 172 174 176-177 179 181-182 185 187-188 198-199 208 256-258 276-277 279-280 282-283 344 346-347 349 351 373-375 400-402 444 456-458 464 480 482 524 527 588 590-592 594-595 597 599 610-611 620-621 623-625 633-634 660 662-663 676-678 680-681 689 691 700 705-710 712-714 716 736 773 781 786 791-793 795-796 798-799 802 814 821 825 830 833 835 840-841 844-845 856-857 859-862 866-867 872-873 876 878 881 886 889 901 911 914 916 920 922-924 926 941-946 954-955 992 1103-1104 1115 1117-1119 1127 1131-1134 1137-1140 1142 1144-1145 1147 1167 1175 1178-1179 1186 1192-1193 1195 1204 1206-1208 1210-1211 1220 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49112-49113 49141https://cdsarc.cds.unistra.fr/viz-bin/moc/J/ApJS/246/9?format=asciiOpticaldefaultJ/ApJS/246/9/catalogThe Catalog of the 5 million converged LAMOST DR5 K giant stars with SLAM-derived stellar labelsrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintObsID[101008/585216248]? LAMOST identifiermeta.idlongnullableindexedRAJ2000Right ascension (J2000) (ra)degpos.eq.ra;meta.mainindexedDEJ2000Declination (J2000) (dec)degpos.eq.dec;meta.mainindexedsnrg[0/998]? g-band signal-to-noise ratio of the spectrastat.snrfloatnullableTeff[-7492/15227] SLAM-predicted effective temperature (teff_slam)Kphys.temperature.effectivefloatindexede_Teff[38.8/241.3] Teff uncertainty (teff_err_slam)Kstat.error;phys.temperature.effectivefloatlogg[-41.7/31.3] SLAM-predicted log of surface gravity (logg_slam)log(cm.s**-2)phys.gravityfloate_logg[0.06/0.7] logg uncertainty (logg_err_slam)log(cm.s**-2)stat.errorfloat[M/H][-21.6/12] SLAM-predicted metallicity (mh_slam)phys.abund.Zdoublee_[M/H][0.02/0.5] Uncertainty in [M/H] (mh_err_slam)stat.error;phys.abundfloat[a/M][-4.2/7.1] SLAM-predicted [{alpha}/M] (am_slam)phys.abundfloate_[a/M][0.01/0.2] Uncertainty in [a/M] (am_err_slam)stat.errorfloat[C/M][-9/7.5] SLAM-predicted [C/M] (cm_slam)phys.abundfloate_[C/M][0.04/0.2] Uncertainty in [C/M] (cm_err_slam)stat.errorfloat[N/M][-9.5/15.5] SLAM-predicted [N/M] (nm_slam)phys.abundfloate_[N/M][0.07/0.3] Uncertainty in [N/M] (nm_err_slam)stat.errorfloatO-C[0.005/243963] rms deviation between the observed and fitted spectra (rmseppix_slam)stat.stdev;instr.det.noise;meta.modelleddoubleK[0/1] K giant star selected (1=yes) (ind_kgiant)meta.codeintD[0.0005/16038]? Distance D (DHo17) (1)stat.fit.paramdoublenullableFl[0/1] APOGEE observation flag (1:observed by APOGEE) (ind_common)meta.codeintSloanDisplay the online SDSS-DR16 data for the nearest objectmeta.ref.urlchar