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What To Do With 1,000,000 Quasars

What To Do With 1,000,000 Quasars. Gordon Richards Drexel University.

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What To Do With 1,000,000 Quasars

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  1. What To Do With 1,000,000 Quasars Gordon Richards Drexel University With thanks to Adam Myers (Illinois), Alex Gray, Ryan Reigel (Georgia Tech), Bob Nichol (Portsmouth), Joe Hennawi (Berkeley), Don Schneider (Penn State), Michael Strauss (Princeton), Alex Szalay (JHU), and a host of other people from the SDSS Collaboration Want winds instead? See arXiv:astro-ph/0603827

  2. Additional References Binaries/Small Scales Myers et al. 2007b, Clustering Analyses of 300,000 Photometrically Classified Quasars. II. The Excess on Very Small Scales, ApJ, 658, 99 Myers et al. 2008, Quasar Clustering at 25 kpc from a Complete Sample of Binaries, ApJ, submitted Hennawi et al. 2006,Binary Quasars in the Sloan Digital Sky Survey: Evidence for Excess Clustering on Small Scales, AJ, 131, 1 Scranton et al. 2005,Detection of Cosmic Magnification with the Sloan Digital Sky Survey, ApJ, 633, 589 Giannantonio et al. 2005,High redshift detection of the integrated Sachs-Wolfe effect, PhysRevD,74f3520 Giannantonio et al. 2008,Combined analysis of the integrated Sachs-Wolfe effect and cosmological implications,arXiv0801.4380 Comic Magnification Integrated Sachs-Wolfe Effect

  3. NEW!! 99 year lease! Name a QUASAR for the low, low price of $99.99. You get: • Over 10 billion stars • 1 supermassive black hole • loads of extras

  4. The Sloan Digital Sky Survey (SDSS) Quasar Sample Spectra of ~100,000 quasars in 10,000 sq. deg. i < 19.1 for z<3.0 i < 20.2 for z>3.0 Both color and radio selection See Richards et al. 2002, AJ, 123, 2945 for details So far: ~77,000 quasars from z=0 to z=6.4. See Schneider et al. 2007, arXiv:0704.0806

  5. Quasar Surveys Status Hasinger et al. 2005

  6. Optimizing Quasar Surveys SDSS . X-ray/IR surveys are deep enough (up to a few 1000 AGN/sq. deg.), but not wide enough. Optical surveys are wide enough, but not deep enough. Need deeper optical surveys and/or larger area X-ray/IR surveys.

  7. Can We Do Better?

  8. Yes, we can.

  9. Why We Need To Do Better

  10. Merger Scenario w/ Feedback Merge gas-rich galaxies, forming buried quasars, feedback expels the gas, revealing the quasar and eventually shutting down accretion. Hopkins et al. 2005

  11. How Can We Test This? • The Quasar Luminosity Function • active lifetime • accretion rate • MBH distribution • Quasar Clustering • L, z dependence • small scales

  12. Quasar Luminosity Function Space density of quasars as a function of redshift and luminosity Typically fit by double power-law Croom et al. 2004

  13. Density Evolution Number of quasars is changing as a function of time.

  14. Luminosity Evolution Space density of quasars is constant. Brightness of individual quasars is changing.

  15. Hopkins et al. 2005 Most QLF models assume they are either “on” or “off” and that there is a mass/luminosity heirarchy. Hopkins et al.: quasar phase is episodic and “all quasars are created equal” (with regard to mass/luminosity).

  16. The SDSS QLF SDSS, though relatively shallow, allows us to determine the QLF from z=0 to z=5 with a single dataset. QLF slope flattens at high-z. Not PDE, PLE Richards et al. (2006)

  17. Understanding the High-z QLF The change of the bright slope in the QLF at high redshift means the distribution of intrinsic luminosities is broader at high redshift. Hopkins et al. 2005 Richards et al. 2006

  18. We Can Do Better Hopkins, Richards, & Hernquist 2007

  19. The Future: Efficient Target Selection + Photo-z’s Current selection techniques for quasars are inefficient in the optical (~50-80% success rate). It takes MUCH longer to take spectra than to get photometry. More efficient (~95%) selection algorithms coupled with accurate photometric redshift techniques can make spectroscopy nearly obsolete.

  20. red Traditional Quasar Selection M z < 2.2 quasars F z > 3 quasars A blue blue red

  21. Spitzer-IRAC (Mid-IR) Selection GAL AGN STAR e.g. Lacy et al. 2004, Stern et al. 2005

  22. How Can We Do Better? Non-Parametric Bayesian Classification with Kernel Density Estimation (aka NBCKDE) Richards et al. 2004, ApJS, Efficient Photometric Selection of Quasars from the SDSS: 100,000 Quasars from DR1, 155, 257

  23. Given two training sets, Here quasarsand stars (non-quasars), and an unknown object, which class is more likely?

  24. “NBC”: Bayes’ (1763) Rule • Where • x = N-D colors • P(Star|x) = probability of being a star, given x • P(x|Star) = probability of x, drawing from stars training set • P(x|QSO) = probability of x, drawing from QSO training set • P(Star) = stellar prior • P(QSO) = quasar prior • P(Star) + P(QSO) = 1 • Star if P(Star|x)>0.5, QSO if P(Star|x)<0.5

  25. “KDE”: Kernel Density Estimation x xi But Naïve KDE is O(N2)

  26. Tree building is O(N log N); usually fast in comparison to the rest of computation Classification of 500k objects in ~900 sec for reasonable bandwidths See Gray, Riegel in Compstat 2006 Dual-tree Method

  27. Separating Quasars from Stars

  28. DR6 Resultsincluding high-z 840,000 – 1,060,000 quasars Richards et al. 2008

  29. Quasar Photo-z z=1.5 u g r i z

  30. Photometric Redshifts Photometric redshifts are 80% accurate to within 0.3 Richards et al. 2001 Weinstein, Richards et al. 2004

  31. SDSS vs. Johnson-Morgan/Kron-Cousins

  32. SDSS+UKIDSS+IRAC z=1.5 Hα plus slope change makes for robust photo-z

  33. LSST LSST corp.

  34. QSO Detection With Time 1955-1990: Slow! Methods include radio/UVX detection A. Myers

  35. QSO Detection With Time 1990-2000: Multi-Fiber Spectrographs/Plate Scanning Machines

  36. QSO Detection With Time 1995-2002: Long-term Surveys 5 years yields ~80,000 QSOs

  37. QSO Detection With Time 2002-2010: Million+ QSOs via Photometric Classification?

  38. Autocorrelation Function () • Red Points are, on average, randomly distributed, black points are clustered • Red points: ()=0 • Black points: ()>0 • Can vary as a function of, e.g., angular distance,  (blue circles) • Red: ()=0 on all scales • Black: () is larger on smaller scales A. Myers

  39. CDM P(k) projected across redshift distribution yields good fit to shape of data. • Linear bias (bQ=1) ruled out at high significance. • Fitting for stellar contamination improves fit on scales larger than a degree. Implied star fraction ~< 5%

  40. For CDM cosmology, quasar bias evolves as a function of redshift (Significance of detection of evolution >99.5% using only DR4 KDE data set). • Detection in good agreement with earlier results from independent spectroscopic data (2dF QSO redshift survey).

  41. Use ellipsoidal collapse model (Sheth, Mo & Tormen, 2001, MNRAS, 323, 1) to turn estimates of bQinto mass of halos hosting UVX quasars. • Find very little evolution in halo mass with redshift. • Our mean halo mass of ~5x1012h-1MSolar is halfway between characteristic masses from Croom et al. (2005, MNRAS, 356, 415) and Porciani et al. (2004, MNRAS, 355, 1010).

  42. Hopkins+05 ApJ, 630, 716 “an observational probe that differentiates quasars based on their host galaxy properties such as … the dependence of clustering of quasars on luminosity, can be used to discriminate our picture from older models.”

  43. Lidz et al. 2006 • Quasars accreting over a wide range of luminosity must be driven by a narrow range of black hole masses • M- relation mean a wide range of quasar luminosities will then occupy a narrow range of MDMH

  44. Luminosity Evolution • Very little dependence of quasar clustering on absolute magnitude of the quasar population (Myers et al. 2007) using large SDSS photometric sample bias Mg

  45. Luminosity Evolution • Similarly from the SDSS+2dF=2SLAQ quasar survey. da Angela et al. 2008

  46. What We (Used To) Expect Galaxies (and their DM halos) grow through hierarchical mergers Quasars inhabit rarer high-density peaks If quasars long lived, their BHs grow with cosmic time MBH-σ relation implies that the most luminous quasars are in the most massive halos. More luminous quasars should be more strongly clustered (b/c sample higher mass peaks). QLF from wide range of e and narrow BH masses range or wide range of BH masses (DMH masses) and narrow e

  47. What We Get Galaxies (and their DM halos) grow through hierarchical mergers, but with “cosmic downsizing” Quasars always turn on in potential wells of a certain size (at earlier times these correspond to relatively higher density peaks). Quasars turn off on timescales shorter than hierarchical merger times, are always seen in similar mass halos (on average). MBH-σ relation then implies that quasars trace similar mass black holes (on average) Thus little luminosity dependence to quasar clustering (L depends on accretion rate more than mass). Need a wide range of accretion efficiencies for a narrow range of MBH to be consistent with QLF.

  48. Conclusions • Identification of large numbers of faint, quasars is possible using novel statistical methods • Use of such methods will be crucial in the LSST era • The resulting samples are extremely useful for testing the merger models of quasars • More to come!

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