J/ApJ/798/122ivo://CDS.VizieR/J/ApJ/798/122doi:10.26093/cds/vizier.17980122CDSMiller A.A.Bloom J.S.Richards J.W.Lee Y.S.Starr D.L.Butler N.R.,Tokarz S.Smith N.Eisner J.A.2015-07-18T13:52:09Z2015-07-18T13:37:18ZCDS support team

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

cds-question@unistra.frvariable-starsmetallicityeffective-temperaturesurveysA fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10^9^ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~fewx10^6^ targets. As we approach the Large Synoptic Survey Telescope era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (T_eff_, logg, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/Multi-Mirror Telescope. In sum, the training set includes ~9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts T_eff_, logg, and [Fe/H] from photometric time-domain observations. Our final optimized model produces a cross-validated rms error (RMSE) of 165K, 0.39dex, and 0.33dex for T_eff_, logg, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125K, 0.37dex, and 0.27dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a {approx}12%-20% improvement in RMSE relative to models trained with single-epoch photometric colors. As an application of our method, we estimate stellar parameters for ~54000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history.2015ApJ...798..122Mhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/798/122CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toVII/260 : The SDSS-DR7 quasar catalog (Schneider+, 2010)VII/233 : The 2MASS Extended sources (IPAC/UMass, 2003-2006)J/AJ/145/13 : Metal-poor stars from SDSS/SEGUE. I. Abundances (Aoki+, 2013)J/ApJS/203/32 : Machine-learned ASAS Classif. Cat. (MACC) (Richards+, 2012)J/AJ/142/112 : KIC photometric calibration (Brown+, 2011)J/MNRAS/414/2602 : Automated classification of HIP variables (Dubath+, 2011)J/AJ/141/90 : SEGUE stellar parameter pipeline. V. (Lee+, 2011)J/ApJ/728/26 : QSO selection based on photom. variability (Macleod+, 2011)J/A+A/494/739 : Automatic classification of OGLE variables (Sarro+, 2009)J/AJ/134/973 : SDSS Stripe 82 star catalogs (Ivezic+, 2007)J/AJ/138/466 : NSVS variables automated classification (Hoffman+, 2009)J/AJ/136/2070 : SEGUE stellar parameter pipeline. III. (Allende Prieto+, 2008)J/AJ/136/2050 : SEGUE stellar parameter pipeline. II. (Lee+, 2008)J/AJ/134/973 : SDSS Stripe 82 Variable Source Catalog (Ivezic+, 2007)J/A+A/475/1159 : Supervised classif. of variable stars (Debosscher+, 2007)J/PASP/110/863 : A Stellar Spectral Flux Library: 1150 - 25000 A (Pickles 1998)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/798/122https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/798/122http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/798/122https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/798/122https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/798/122http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJ/798/122GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/ApJ/798/122/table3 (SEGUE Stellar Parameters Pipeline (SSPP) stellar parameters for Hectospec targets)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJ/798/122/table3?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJ/798/122/table3?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJ/798/122/table3?GETtext/xml+votable180.050000true306.74429694-0.018936110.005555555555555556Cone search capability for table J/ApJ/798/122/table5 (Final model predictions for the University of Washington variable source catalog (UWVSC) sources)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJ/798/122/table5?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJ/798/122/table5?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJ/798/122/table5?GETtext/xml+votable180.050000true1.14551-0.887603890.0055555555555555564/1189 1193 1194 1877 1878 1881 5/3072 4437 4438 4439 4441 4442 4444 4453 4454 4457 4458 4460 4500 4501 4502 4504 4505 4506 4517 4518 4520 4521 4522 4528 4693 4694 4696 4697 4698 4709 4710 4713 4714 4760 4761 4762 4764 4800 4864 5785 5786 5797 5798 5799 5800 5801 5802 5808 7503 7507 7516 7523 7532 7536 12287 6/0 1 2 12292 12296 17405 17406 17407 17661 17662 17663 17719 17723 17725 17726 17727 17741 17742 17743 17745 17746 17747 17761 17762 17763 17772 17773 17774 17805 17806 17807 17809 17810 17811 17820 17821 17822 17825 17826 17827 17836 17837 17838 17856 17857 17858 17981 17982 17983 17997 17998 17999 18012 18013 18014 18028 18029 18030 18032 18033 18034 18061 18062 18063 18065 18066 18067 18076 18077 18078 18092 18093 18094 18096 18097 18098 18120 18176 18177 18178 18180 18184 18685 18686 18687 18747 18749 18750 18751 18765 18766 18767 18769 18770 18771 18780 18781 18782 18796 18797 18798 18800 18801 18802 18823 18829 18830 18831 18833 18834 18835 18844 18845 18846 18849 18850 18851 18860 18861 18862 18864 18865 18866 18872 18880 18881 18882 18888 19005 19006 19007 19021 19022 19023 19052 19053 19054 19085 19086 19087 19120 19121 19122 19128 19136 19137 19138 19140 19144 19204 19208 23095 23099 23101 23102 23103 23118 23119 23137 23138 23139 23148 23149 23150 23152 23154 23179 23181 23182 23183 23185 23186 23187 23212 23213 23214 23216 23217 23218 23224 30007 30011 30023 30027 30068 30072 30087 30116 30117 30119 30125 30132 30148 30152 36861 36862 36863 49143 49147https://cdsarc.cds.unistra.fr/viz-bin/moc/J/ApJ/798/122?format=asciiOpticaldefaultJ/ApJ/798/122/table3SEGUE Stellar Parameters Pipeline (SSPP) stellar parameters for Hectospec targetsP[1-9. 10] Target priority for Hectospec observations (1)meta.codecharrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintIDSource identifier in the UWVSC (G1)meta.id;meta.mainintFlagsAnalysis flags returned by the SSPP (2)meta.code.errorcharTeff[4006/8643]? Effective temperatureKphys.temperature.effectivefloatnullablee_Teff? Uncertainty in TeffKstat.error;phys.temperature.effectivefloatnullableo_Teff[0/10] Number of methods used by SSPP to derive Teffmeta.numberintlog(g)[0.7/5]? Log surface gravitylog(cm.s**-2)phys.gravitydoublenullablee_log(g)[0/1.6]? Uncertainty in log(g)log(cm.s**-2)stat.errordoublenullableo_log(g)[0/7] Number of methods used by SSPP to derive log(g)meta.numberint[Fe/H][-3.4/0.7]? Metallicitylog(Sun)phys.abund.Zdoublenullablee_[Fe/H][0/1.1]? Uncertainty in [Fe/H]log(Sun)stat.error;phys.abund.Fedoublenullableo_[Fe/H][0/9] Number of methods used by SSPP to derive [Fe/H]meta.numberintModDisplay the final model predictions for this source (table 5) if anymeta.codeintnullableSloanDisplay the online SDSS data for the nearest sourcemeta.ref.urlchar_RAPositions from SDSS Stripe 82 Variable Source Catalog (J/AJ/134/973) (right ascension part)degpos.eq.ra;meta.main_DEPositions from SDSS Stripe 82 Variable Source Catalog (J/AJ/134/973) (declination part)degpos.eq.dec;meta.mainJ/ApJ/798/122/table5Final model predictions for the University of Washington variable source catalog (UWVSC) sourcesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintIDSource identifier in the UWVSC (G1)meta.id;meta.mainintRAJ2000Right Ascension in decimal degrees (J2000)degpos.eq.ra;meta.mainindexedDEJ2000Declination in decimal degrees (J2000)degpos.eq.dec;meta.mainindexedTeff[4116/8422] Effective temperatureKphys.temperature.effectivefloatlog(g)[2.3/4.6] Log surface gravitylog(cm.s**-2)phys.gravityfloat[Fe/H][-2.3/-0.01] Metallicitylog(Sun)phys.abund.ZfloatSSPPDisplay the SSPP parameters for this source (table 3) if anymeta.codeintnullableSloanDisplay the online SDSS data for the nearest sourcemeta.ref.urlchar