In addition to optical photometry of unprecedented quality, the Sloan Digital Sky Survey (SDSS) is also producing a massive spectroscopic database. They discuss determination of stellar parameters, such as effective temperature, gravity and metallicity from SDSS spectra, describe correlations between kinematics and metallicity, and study their variation as a function of the position in the Galaxy. They show that stellar parameter estimates by Beers et al. show a good correlation with the position of a star in the g-r vs. u-g color-color diagram, thereby demonstrating their robustness as well as a potential for photometric parameter estimation methods. Using Beers et al. parameters, they find that the metallicity distribution of the Milky Way stars at a few kpc from the galactic plane is bimodal with a local minimum at [Z/Z{sub {circle_dot}}] {approx} -1.3. The median metallicity for the low-metallicity [Z/Z{sub {circle_dot}}] &lt; =1.3 subsample is nearly independent of Galactic cylindrical coordinates R and z, while it decreases with z for the high-metallicity [Z/Z{sub {circle_dot}}] &gt; -1.3 sample. they also find that the low-metallicity sample has {approx} 2.5 times larger velocity dispersion and that it does not rotate (at the {approx} 10 km/s level), while the rotational velocity of the high-metallicity sample decreases smoothly with the height above the galactic plane

/Washington U., Seattle, Astron. Dept. /LBL, Berkeley /Johns Hopkins U. /Princeton U. /Michigan State U. /Texas U. /Texas Tech. /UC, Santa Cruz /Fermilab /Naval Observ., Flagstaff /Drexel U.

Allende Prieto, C.

Beers, T.

Bond, N.

Ivezic, Zeljko

Juric, M.

Lee, Y.Sun

Lupton, R.

Schlegel, D.

Sivarani, T.

Uomoto, A.

Wilhelm, R.

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FERMILAB-CONF-07-013-CD2 Ivezic´ et al.: SDSS spectroscopic survey of starsgalaxies, 90,000 quasars, and 155,000 stars.A pair of dual multi-object fiber-fed spectro-graphs on the same telescope are used to take640 simultaneous spectra (spectroscopic plateshave a radius of 1.49 degrees), each with wave-length coverage 3800–9200 Å and spectral res-olution of∼2000, and with a signal-to-noise ra-tio of >4 per pixel at g=20.2.The spectra are targeted and automaticallyprocessed by three pipelines:– target: Target selection and tiling– spectro2d: Extraction of spectra, sky sub-traction, wavelength and flux calibration,combination of multiple exposures– spectro1d: Object classification, red-shifts determination, measurement of linestrengths and line indicesFor each object in the spectroscopic survey,a spectral type, redshift (or radial velocity),and redshift error is determined by matchingthe measured spectrum to a set of templates.The stellar templates are calibrated using theELODIE stellar library. Random errors for theradial velocity measurements are a strong func-tion of spectral type, but are usually < 5 kms−1for stars brighter than g ∼ 18, rising sharply to∼25 kms−1 for stars with g = 20. Using a sam-ple of multiply-observed stars, Pourbaix et al.(2005) estimate that these errors may be un-derestimated by a factor of ∼1.5.3. The utility and analysis of SDSSstellar spectraThe SDSS stellar spectra are used for:1. Calibration of observations2. More accurate and robust source identifi-cation than that based on photometric dataalone3. Accurate stellar parameters estimation4. Radial velocity for kinematic studies3.1. Calibration of SDSS spectraStellar spectra are used for the calibration ofall SDSS spectra. On each spectroscopic plate,16 objects are targeted as spectroscopic stan-dards. These objects are color-selected to beFig. 1. A test of the quality of spectrophotometriccalibration. Each thin curve shows a spectrum of ahot white dwarf (which were not used in calibration)divided by its best-fit model. The thick red curve isthe median of these curves.similar in spectral type to the SDSS primarystandard BD+17 4708 (an F8 star). The spec-trum of each standard star is spectrally typedby comparing with a grid of theoretical spec-tra generated from Kurucz model atmospheresusing the spectral synthesis code SPECTRUM(Gray et al. 2001). The flux calibration vec-tor is derived from the average ratio of eachstar and its best-fit model, separately for eachof the 2 spectrographs, and after correctingfor Galactic reddening. Since the red and bluehalves of the spectra are imaged onto separateCCDs, separate red and blue flux calibrationvectors are produced. The spectra from multi-ple exposures are then combined with bad pixelrejection and rebinned to a constant dispersion.The absolute calibration is obtained by tyingthe r-band fluxes of the standard star spectra tothe fiber magnitudes output by the photomet-ric pipeline (fiber magnitudes are corrected toa constant seeing of 2 arcsec, with accountingfor the contribution of flux from overlappingobjects in the fiber aperture).To evaluate the quality of spectrophoto-metric calibration on scales of order 100Å,the calibrated spectra of a sample of 166 hotDA white dwarfs drawn from the SDSS DR1White Dwarf Catalog (Kleinman et al. 2004)are compared to theoretical models (DA whitedwarfs are useful for this comparison becausethey have simple hydrogen atmospheres thatcan be accurately modeled). Figure 1 shows theIvezic´ et al.: SDSS spectroscopic survey of stars 3results of dividing each white dwarf spectrumby its best fit model. The median of the curvesshows a net residual of order 2% at the bluestwavelengths.Another test of the quality of spectropho-tometric calibration is provided by the com-parison of imaging magnitudes and thosesynthesized from spectra, for details seeVanden Berk et al. (2004) and Smolcˇic´ et al.(2006). With the latest reductions1 the twotypes of magnitudes agree with an rms of∼0.05 mag.3.2. Source IdentificationSDSS stellar spectra have been success-fully used for confirmation of unresolved bi-nary stars, low-metallicity stars, cold whitedwarfs, L and T dwarfs, carbon stars, etc.For more details, we refer the reader toAdelman-McCarthy et al. (2006) and refer-ences therein.3.3. Stellar Parameters EstimationSDSS stellar spectra are of sufficient qual-ity to provide robust and accurate stellar pa-rameters such as effective temperature, grav-ity, metallicity, and detailed chemical compo-sition. Here we study a correlation betweenthe stellar parameters estimated by Beers et al.group (Allende Prieto et al. 2006) and the po-sition of a star in the g − r vs. u− g color-colordigram.Figure 2 shows that the effective tempera-ture determines the g− r color, but has negligi-ble impact on the u − g color. The expressionlog(Teff/K) = 3.877 − 0.26 (g − r) (1)provides correct spectroscopic temperaturewith an rms of only 2% (i.e. about 100-200 K)for the −0.3 < g − r < 1.0 color range. Whilethe median metallicity shows a more complexbehavior as function of the u − g and g − r col-ors, it can still be utilized to derive photometricmetallicity estimate. For example, for stars at1 DR5/products/spectra/spectrophotometry.html,where DR5=http://www.sdss.org/dr5the blue tip of the stellar locus (u − g < 1), theexpression[Z/Z⊙] = 5.11 (u − g) − 6.33 (2)reproduces the spectroscopic metallicity withan rms of only 0.3 dex.These encouraging results are importantfor studies based on photometric data alone,and also demonstrate the robustness of param-eters estimated from spectroscopic data.3.4. Metallicity Distribution andKinematicsDue to large sample size and faint limitingmagnitude (g ∼ 20), the SDSS stellar spec-tra are an excellent resource for studying theMilky Way metallicity distribution, kinematicsand their correlation all the way to the bound-ary between the disk and halo at several kpcabove the Galactic plane (Juric´ et al. 2006).Here we present some preliminary results thatillustrate the ongoing studies.3.4.1. The Bimodal MetallicityDistributionIn order to minimize various selection effects,we study a restricted sample of ∼10,000 bluemain-sequence stars defined by 14.5 < g <19.5, 0.7 < u−g < 2.0 and 0.25 < g−r < 0.35.The last condition selects stars with the ef-fective temperature in the narrow range 6000-6500 K. These stars are further confined to themain stellar locus by |s| < 0.04, where the scolor, described by Ivezic´ et al. (2004), is per-pendicular to the locus in the g − r vs. u − gcolor-color diagram (c.f. Fig. 2). We estimatedistances using a photometric parallax relationderived by Juric´ et al. (2006).The metallicity distribution for stars fromthis sample that are at a few kpc from thegalactic plane is clearly bimodal (see the mid-dle panel in Fig. 3), with a local minimum at[Z/Z⊙] ∼ −1.3. Motivated by this bimodality,we split the sample into low- (L) and high-metallicity (H) subsamples and analyze thespatial variation of their median metallicity. Asshown in the bottom panel in Fig. 3, the me-dian metallicity of the H sample has a much4 Ivezic´ et al.: SDSS spectroscopic survey of stars0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.80.8 0.9 1 1.1 1.2 1.30.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.80.8 0.9 1 1.1 1.2 1.3Fig. 2. The top left panel shows the median effective temperature estimated from spectra of ∼40,000 starsas a function of the position in the g − r vs. u − g diagram based on imaging data. The temperature ineach color-color bin is linearly color-coded from 4000 K (red) to 10,000 K (blue). The bottom left panel isanalogous except that it shows the blue tip of the stellar locus with the effective temperature in the range5300 K to 6700 K. The two right panels are analogous to the left panels, except that they show the medianmetallicity, linearly color-coded from -0.5 (red) to -2.5 (blue).larger gradient in the z direction (distance fromthe plane), than in the R direction (cylindricalgalactocentric radius). In contrast, the medianmetallicity of the L sample shows negligiblevariation with the position in the Galaxy (<0.1dex within 4 kpc from the Sun) and the wholedistribution appears Gaussian, with the widthof 0.35 dex and centered on [Z/Z⊙] = −1.75.The decrease of the median metallicitywith z for the H sample is well described by[Z/Z⊙] = −0.65 − 0.15 Z/kpc for Z < 1.5 kpcand [Z/Z⊙] = −0.80−0.05 Z/kpc for 1.5 < Z <Ivezic´ et al.: SDSS spectroscopic survey of stars 55 6 7 8 9 10 11 12 13R (kpc) Fig. 3. The dots in the top panel show the metallic-ity of stars with 0.25 < g − r < 0.35 as a func-tion of the height above the Galactic plane. Thelarge symbols are the medians evaluated separatelyfor the low-metallicity ([Z/Z⊙] < −1.3) and high-metallicity ([Z/Z⊙] > −1.3) subsamples, and thedashed lines show the 2σ envelopes around the me-dian. The histogram in the middle panel illustratesthe bimodality of metallicity distribution for starswith heights above the galactic plane between 1kpc and 2 kpc. The two solid lines are the best-fitGaussians, and the dashed line is their sum. Thedependence of the median metallicity for the high-metallicity subsample on the cylindrical galactic co-ordinates R and z is shown in the bottom panel (lin-early color-coded from −1.2 to −0.5, blue to red).Note that the z gradient is much larger than the Rgradient. For the low-metallicity subsample, thesegradients are negligible.Fig. 4. The dots in the top panel show the radialvelocity as a function of metallicity for stars with0.25 < g − r < 0.35 and 160 < l < 200 (towardsanticenter, where the radial velocity corresponds tothe vR velocity component). The large symbols arethe medians evaluated in narrow metallicity bins,and the dashed lines show the 2σ envelopes aroundthe median. The radial velocity distributions for thelow- and high-metallicity subsamples (separated by[Z/Z⊙] = −1.3) are shown in the bottom panel, to-gether with the best-fit Gaussians (dashed lines) andtheir parameters. Note that the velocity dispersion is∼2.5 times larger for the low-metallicity subsample.4 kpc (see the top panel in Fig. 3). For Z < 1kpc, most stars have [Z/Z⊙] > −1.3 and pre-sumably belong to thin and thick disks (for arecent determination of the stellar number den-sity based on SDSS data that finds two expo-nential disks, see Juric´ 2006). The decrease ofthe median metallicity with z for the H samplecould thus be interpreted as due to the increas-ing fraction of the lower-metallicity thick diskstars. However, it is puzzling that we are un-able to detect any hint of the two populations.An analogous absence of a clear distinction be-tween the thin and thick disks is also foundwhen analyzing the radial velocity distribution.6 Ivezic´ et al.: SDSS spectroscopic survey of starsFig. 5. The top panel shows the median radial ve-locity in Lambert projection for stars from the low-metallicity [Z/Z⊙] < −1.3 subsample, which haveb > 0 and are observed at distances between 2.5 kpcand 3.5 kpc. The radial velocity is linearly color-coded from -220 km/s to 220 km/s (blue to red,green corresponds to 0 km/s). The bottom panel isanalogous, except that the radial velocity measure-ments are corrected for the canonical solar motionof 220 km/s towards (l = 90, b = 0).3.4.2. The Metallicity–KinematicsCorrelationIn addition to the bimodal metallicity distribu-tion, the existence of two populations is alsosupported by the radial velocity distribution.As illustrated in Fig. 4, the low-metallicityLow-metallicity, velocity dispersion, 40-160 km/sFig. 6. Analogous to Fig. 5, except that the velocitydispersion is shown (color-coded from 40 km/s to160 km/s).Fig. 7. A comparison of the radial velocity dis-tribution for low-metallicity stars observed towardsl ∼ 90 and l ∼ 180. Note that the subsample ob-served towards the anti-center has a large velocitydispersion, in agreement with Figure 6.component has about 2.5 times larger veloc-ity dispersion than the high-metallicity com-ponent. Of course, this metallicity–kinematicscorrelation was known since the seminal paperby Eggen, Lynden-Bell & Sandage (1962),but here it is reproduced using a ∼100 timeslarger sample that probes a significantly largerGalaxy volume.3.4.3. The Global Behavior ofKinematicsThe large sample size enables a robust searchfor anomalous features in the global behaviorof kinematics, e.g. Sirko et al. (2004). For ex-Ivezic´ et al.: SDSS spectroscopic survey of stars 7ample, while the variation of the median radialvelocity for the low-metallicity subsample iswell described by the canonical solar motion(Fig. 5), we find an isolated ∼1000 deg2 largeregion on the sky where the velocity disper-sion is larger (130 km/s) than for the rest ofthe sky (100 km/s), see Figs. 6 and 7. This isprobably not a data artefact because the disper-sion for the high-metallicity subsample doesnot show this effect. Furthermore, an analy-sis of the proper motion database constructedby Munn et al. (2004) finds that the samestars also have anomalous (non-zero) rotationalvelocity in the same sky region (Bond et al.2006). This kinematic behavior could be dueto the preponderance of stellar streams in thisregion (towards the anti-center, at high galac-tic latitudes, and at distances of several kpc).Bond et al. (2006) also find, using a sampleof SDSS stars for which all three velocitycomponents are known, that the halo (low-metallicity sample) does not rotate (at the ∼10km/s level), while the rotational velocity ofthe high-metallicity sample decreases with theheight above the galactic plane.4. ConclusionsWe show that stellar parameter estimates byBeers et al. show a good correlation with theposition of a star in the g − r vs. u − g color-color diagram, thereby demonstrating their ro-bustness as well as a potential for photometricstellar parameter estimation methods. We findthat the metallicity distribution of the MilkyWay stars at a few kpc from the galactic planeis clearly bimodal with a local minimum at[Z/Z⊙] ∼ −1.3. The median metallicity for thelow-metallicity [Z/Z⊙] < −1.3 subsample isnearly independent of Galactic cylindrical co-ordinates R and z, while it decreases with z forthe high-metallicity [Z/Z⊙] > −1.3 sample. Wealso find that the low-metallicity sample has∼2.5 times larger velocity dispersion.The samples discussed here are sufficientlylarge to constrain the global kinematic behav-ior and search for anomalies. For example, wefind that low-metallicity stars observed at highgalactic latitudes at distances of a few kpc to-wards Galactic anticenter have anomalouslylarge velocity dispersion and a non-zero rota-tional component in a well-defined∼1000 deg2large region, perhaps due to stellar streams.These preliminary results are only brief il-lustrations of the great potential of the SDSSstellar spectroscopic database. This datasetwill remain a cutting edge resource for a longtime because other major ongoing and up-coming stellar spectroscopic surveys are eithershallower (e.g. RAVE), or have a significantlynarrower wavelength coverage (GAIA).Acknowledgements. Funding for the SDSS andSDSS-II has been provided by the Alfred P.Sloan Foundation, the Participating Institutions, theNational Science Foundation, the U.S. Departmentof Energy, NASA, the Japanese Monbukagakusho,the Max Planck Society, and the Higher EducationFunding Council for England.ReferencesAdelman-McCarthy, J.K., et al. 2006, ApJS,162, 38Allende Prieto, C., et al. 2006, ApJ, 636, 804Bahcall, J.N. & Soneira, R.M. 1980, ApJSS,44, 73Bond, N., et al. 2006, in preparationEggen, O.J., Lynden-Bell, D. & Sandage, A.R.1962, ApJ, 136, 748Gilmore, G., Wyse, R.F.G. & Kuijken, K.1989, ARA&A, Volume 27, pp. 555-627Gray, R.O., Graham, P.W. & Hoyt, S.R. 2001,AJ, 121, 2159Ivezic´, ˇZ., et al. 2004, AN, 325, 583Juric´, M., et al. 2006, submitted to AJKleinman, S.J., et al. 2004, ApJ, 607, 426Majewski, S.R. 1993, ARA&A, 31, 575Munn, J.A., et al. 2004, AJ, 127, 3034Pourbaix, D., et al. 2005, A&A, 444, 643Sirko, E., et al. 2004, AJ, 127, 914Smolcˇic´, V., et al. 2006, accepted to MNRASStoughton, C., et al. 2002, AJ, 123, 485Vanden Berk, D.E., et al. 2004, ApJ, 601, 692York, D.G., et al. 2000, AJ, 120, 1579

SDSS spectroscopic survey of stars

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SDSS spectroscopic survey of stars

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