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/0603827arXiv:astro-ph/0603827

2. Additional References 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 Binaries/Small Scales Comic Magnification Integrated Sachs-Wolfe Effect

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

6. 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 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

7. Quasar Surveys Status Hasinger et al. 2005

8. Optimizing Quasar Surveys 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. SDSS Need deeper optical surveys and/or larger area X-ray/IR surveys..

9. Can We Do Better?

10. Yes, we can.

11. Why We Need To Do Better

12. 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

13. How Can We Test This? The Quasar Luminosity Function active lifetime accretion rate M BH distribution Quasar Clustering L, z dependence small scales

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

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

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

17. 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).

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

19. 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

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

21. 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.

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

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

24. 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

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

26. “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

27. “KDE”: Kernel Density Estimation x xixi But Naïve KDE is O(N 2 )

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

29. Separating Quasars from Stars

30. 840,000 – 1,060,000 quasars DR6 Results including high-z Richards et al. 2008

31. Quasar Photo-z ugriz z=1.5

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

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

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

35. LSST LSST corp.

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

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

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

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

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

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

42. 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).

43. Use ellipsoidal collapse model (Sheth, Mo & Tormen, 2001, MNRAS, 323, 1) to turn estimates of b Q into mass of halos hosting UVX quasars. Find very little evolution in halo mass with redshift. Our mean halo mass of ~5x10 12 h -1 M Solar is halfway between characteristic masses from Croom et al. (2005, MNRAS, 356, 415) and Porciani et al. (2004, MNRAS, 355, 1010).

44. 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.”

45. 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 M DMH

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

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

48. What We (Used To) Expect 1.Galaxies (and their DM halos) grow through hierarchical mergers 2.Quasars inhabit rarer high-density peaks 3.If quasars long lived, their BHs grow with cosmic time 4.M BH -σ relation implies that the most luminous quasars are in the most massive halos. 5.More luminous quasars should be more strongly clustered (b/c sample higher mass peaks). 6.QLF from wide range of e and narrow BH masses range or wide range of BH masses (DMH masses) and narrow e

49. What We Get 1.Galaxies (and their DM halos) grow through hierarchical mergers, but with “cosmic downsizing” 2.Quasars always turn on in potential wells of a certain size (at earlier times these correspond to relatively higher density peaks). 3.Quasars turn off on timescales shorter than hierarchical merger times, are always seen in similar mass halos (on average). 4.M BH -σ relation then implies that quasars trace similar mass black holes (on average) 5.Thus little luminosity dependence to quasar clustering (L depends on accretion rate more than mass). 6.Need a wide range of accretion efficiencies for a narrow range of MBH to be consistent with QLF.

50. 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!

51. Additional References 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 Binaries/Small Scales Comic Magnification Integrated Sachs-Wolfe Effect

52. Bolometric QLF Optical and X- ray alone may not fully describe the QLF, but combined, they do (at least the answers are consistent). Hopkins, Richards, & Hernquist 2007

53. What Does Evolution in Bias Mean? Quasars at high redshift “turned on” in environments which are now clustered far more than typical galaxy environments.Quasars at high redshift “turned on” in environments which are now clustered far more than typical galaxy environments. We don’t see them in these environments nearer the present day. They’ve “turned off”.We don’t see them in these environments nearer the present day. They’ve “turned off”. Can use Press-Schecter formalism to turn quasar bias into mass of host halos…Can use Press-Schecter formalism to turn quasar bias into mass of host halos…

54. Quasars and CosmologyJanuary 22nd, 2008, UIUC What the observations tell us about quasars as a cosmological population Quasar lifetimes ~10 7 years. Very short. Several 100 quasar lifetimes between z=2 and z=1.Quasar lifetimes ~10 7 years. Very short. Several 100 quasar lifetimes between z=2 and z=1. Quasars occupy similar mass host halos (M DMH ) at every redshift (z < 3).Quasars occupy similar mass host halos (M DMH ) at every redshift (z < 3). Quasars accrete at a wide range of luminosities for a narrow range of black hole masses (M BH ).Quasars accrete at a wide range of luminosities for a narrow range of black hole masses (M BH ).

55. Quasars and CosmologyJanuary 22nd, 2008, UIUC Halos merge in a hierarchical model Lacey & Cole (1993)Lacey & Cole (1993) Typical quasar hosts double in mass every Gyr or soTypical quasar hosts double in mass every Gyr or so Constancy of quasar host halo mass thus limits quasar lifetime to around 10 6.5 to 10 7.5 yrsConstancy of quasar host halo mass thus limits quasar lifetime to around 10 6.5 to 10 7.5 yrs Time Mass Time for 2x Mass

56. Quantifying Quasar bias How to deal with photometric redshifts? Wide photoz bins have well-controlled ensemble spectroz distributions (particularly over around 1 < z < 2)

57. Quasar Clustering Evolution Linear Theory (how matter perturbations grow and cluster with time) says objects that cluster like z=1.5 quasars would be far more clustered than galaxies by z=0 Ratio of quasar autocorrelation to that of underlying matter is the quasar bias, b Q

58. Giannantonio et al. 2006 (astro-ph/0607572) WMAP3-photoQSO WMAP3 best fit Detection (>2  ) of DE at z>1 0.075<  m <0.45 -1.15<w<-0.76

59. Evolution of DE w=-1 survives another (weak) test But rules out models with  D  (z=1.5) < 0.5 Important for tests of modified gravity theories (Song et al. 2006)

60. Quasars and CosmologyJanuary 22nd, 2008, UIUC Quasar Clustering Evolution Galaxies and quasars appear to cluster in a very similar manner.Galaxies and quasars appear to cluster in a very similar manner. BUT galaxies are at z~0.2 and QSOs are at z~1.5?BUT galaxies are at z~0.2 and QSOs are at z~1.5? Over half of cosmic history is spanned between these redshifts. Why is there no evolution?Over half of cosmic history is spanned between these redshifts. Why is there no evolution?

61. Quasars and CosmologyJanuary 22nd, 2008, UIUC Simulations directly imply quasar lifetime (e.g., Hopkins et al. 2007)Simulations directly imply quasar lifetime (e.g., Hopkins et al. 2007) Can derive quasar energy output for a simulated quasarCan derive quasar energy output for a simulated quasar But how to convert this to a cosmological population of quasars?But how to convert this to a cosmological population of quasars? The Merger Hypothesis

62. Quasars and CosmologyJanuary 22nd, 2008, UIUC Merger models reproduce luminosity (in-) dependence of quasar clustering

63. Quasars and CosmologyJanuary 22nd, 2008, UIUC Blow up of bias constraints, from SDSS DR4 photometric quasar data (Myers et al. 2007) and high redshift AGN data (Adelberger & Steidel 2005) Weak luminosity dependence agrees with merger-driven models from Hopkins et al. (2007) “Light bulb” models ruled out Merger models reproduce luminosity (in-) dependence of quasar clustering

64. Quasars and CosmologyJanuary 22nd, 2008, UIUC Richards et al. ( 2004, ApJS, 155, 257) Spot the photometrically classified quasar….anyone?

65. Quasars and CosmologyJanuary 22nd, 2008, UIUC Spot the photometrically classified quasar….anyone? It’s there… Richards et al. ( 2004, ApJS, 155, 257)

66. Quasars and CosmologyJanuary 22nd, 2008, UIUC Spot the photometrically classified quasar….anyone? It’s there… …actually, they’re there. Richards et al. ( 2004, ApJS, 155, 257)

67. Quasars and CosmologyJanuary 22nd, 2008, UIUC Single power law is good fit over 4 orders of magnitude Although we find bias is scale-independent (b Q ~2.4) over 100h -1 kpc to 20h -1 Mpc comoving scales, we find significantly larger bias values (b Q ~4) on small scales (r < 100h -1 kpc) Extra bias cancels out reduced power on small scales in halo model P(k) Small scale excess

68. Quasars and CosmologyJanuary 22nd, 2008, UIUC Small scale excess Hennawi et al. (2006) Similar but larger excess seen at r < 100h -1 kpc in spectroscopic sample of close quasar pairs (“binary quasars”) from SDSS DR3. Hennawi’s sample is heterogeneous and incomplete. but then our measurement is based on a photometric sample. Hennawi et al. (2006)

69. Quasars and CosmologyJanuary 22nd, 2008, UIUC Spectroscopic follow up of KDE selected quasar pairs produced a complete sample of binary quasars. Use these to measure the clustering of a complete sample of quasars in real space on small scales. Basically consistent with Hennawi et al. but still about 2  lower. Small scale excess

70. Quasars and CosmologyJanuary 22nd, 2008, UIUC What is the excess of quasar pairs on small scales telling us? Since the first hints of a measured excess of quasar pairs on angular scales comparable to one or two galaxy radii, the thought has been that these are genuine binary quasars, i.e., gravitationally bound objects orbiting each otherSince the first hints of a measured excess of quasar pairs on angular scales comparable to one or two galaxy radii, the thought has been that these are genuine binary quasars, i.e., gravitationally bound objects orbiting each other Fits in with the merger hypothesis….binary quasars are the nuclei of two galaxies igniting in a mergerFits in with the merger hypothesis….binary quasars are the nuclei of two galaxies igniting in a merger Worth remembering, however, that their typically measured redshift difference/orbital velocity of 2000 km/s is equivalent to nearly 10 Mpc if interpreted as a distance!Worth remembering, however, that their typically measured redshift difference/orbital velocity of 2000 km/s is equivalent to nearly 10 Mpc if interpreted as a distance!

71. Quasars and CosmologyJanuary 22nd, 2008, UIUC Are binaries quasars ignited pre-merger? Probably notProbably not The timescale in a merger where the nuclei of both galaxies are visible as quasars is ~0.01% of a typical quasar lifetimeThe timescale in a merger where the nuclei of both galaxies are visible as quasars is ~0.01% of a typical quasar lifetime Further, only ~10% of mergers are sufficiently face on that we see a quasar in the optical in both galaxiesFurther, only ~10% of mergers are sufficiently face on that we see a quasar in the optical in both galaxies

72. Quasars and CosmologyJanuary 22nd, 2008, UIUC Are binaries quasars ignited pre-merger? So, 1/100000th of the quasar lifetime spent as a binarySo, 1/100000th of the quasar lifetime spent as a binary However, we find ~40 binaries in a sample of ~300000 quasars so not too far offHowever, we find ~40 binaries in a sample of ~300000 quasars so not too far off Can turn this around….if we could measure the fraction of genuine binaries, we could constrain timescales in merger simulationsCan turn this around….if we could measure the fraction of genuine binaries, we could constrain timescales in merger simulations

73. Quasars and CosmologyJanuary 22nd, 2008, UIUC More likely suggestion (Hopkins et al. 2007)More likely suggestion (Hopkins et al. 2007) The probability of a merger occurring increases with the local density of objects on small scalesThe probability of a merger occurring increases with the local density of objects on small scales If quasars are merger-driven, they will be found in major- prone environmentsIf quasars are merger-driven, they will be found in major- prone environments Are binaries quasars ignited pre-merger?

74. Quasars and CosmologyJanuary 22nd, 2008, UIUC Hopkins et al. (2007) Associating quasars with overdense, merger-prone environments predicts enhanced clustering at r < 100h -1 kpcAssociating quasars with overdense, merger-prone environments predicts enhanced clustering at r < 100h -1 kpc There should be no evolution in b Q ( There should be no evolution in b Q (

75. Quasars and CosmologyJanuary 22nd, 2008, UIUC The small scale clustering of quasars still directly implies a merger origin! Beautifully illustrated in Serber et al. (2006) and Strand et al. (2007)Beautifully illustrated in Serber et al. (2006) and Strand et al. (2007) Cross-correlate galaxies and quasars in SDSSCross-correlate galaxies and quasars in SDSS Small scale excess of quasar clustering is a function of luminosity not quasar/AGN typeSmall scale excess of quasar clustering is a function of luminosity not quasar/AGN type

76. Quasars and CosmologyJanuary 22nd, 2008, UIUC The small scale clustering of quasars still directly implies a merger origin! Implication is that quasars form in merger-prone environments but lower- luminosity AGN don’tImplication is that quasars form in merger-prone environments but lower- luminosity AGN don’t Differences between quasars and lower- luminosity AGN are cosmologicalDifferences between quasars and lower- luminosity AGN are cosmological differences within quasar and AGN types are not cosmological (orientation traditionally explains these)differences within quasar and AGN types are not cosmological (orientation traditionally explains these)

77. Quasar Luminosity Function Croom et al. 2004 Space density of quasars as a function of redshift and luminosity e.g., Richards et al. 2006

78. LDDE Number of quasars is changing as a function of time, as a function of luminosity

79. Luminosity-Dependent Density Evolution Ueda et al. (2003) AKA: Comsic Downsizing

80. A Caveat: Jiang et al. 2007 z=6 QLF not flat? (Or steepens again after flattening?) Fontanot also argue that z~4 flatness is instead due to selection function correction.

81. A quasar is a galaxy

82. A quasar is a galaxy in which accretion onto a supermassive black hole produces copious amounts of non-stellar radiation over the entire electromagnetic spectrum; this light dwarfs the light from the galaxy itself. L ~ 10 43-46 ergs/s M ~ 10 6-9 M sun Quasar = active galactic nuclei = AGN = Seyfert etc.

83. H+05 ApJ,630, 716 “In our interpretation, the bright and faint ends of the LF correspond statistically to similar mixes of galaxies, but in various stages of evolution. However, in all other competing scenarios, the QLF is directly related to the mass of the host galaxy. Therefore, an observational probe that differentiates quasars based on their host galaxy properties such as, for example, the dependence of clustering of quasars on luminosity, can be used to discriminate our picture from older models.”

84. QLF Comparison

85. Quasar Luminosity Function Croom et al. 2004 Space density of quasars as a function of redshift and luminosity e.g., Richards et al. 2006

86. Quasar Identification via Non-parametric Bayesian Classification Perform KDE (kernel density estimation) for the classes (e.g., quasars and stars) represented in 4D color spacePerform KDE (kernel density estimation) for the classes (e.g., quasars and stars) represented in 4D color space For each unknown object, choose the most probable class according to Bayes rule (and priors)For each unknown object, choose the most probable class according to Bayes rule (and priors) Naïve KDE is O(N 2 )Naïve KDE is O(N 2 ) A fast ~O(N) dual-tree method is possible; inspired by Barnes-Hut, among othersA fast ~O(N) dual-tree method is possible; inspired by Barnes-Hut, among others Also Richards et al. 2004, ApJS, 55, 257Also Richards et al. 2004, ApJS, 55, 257

87. Comparing Probability Densities 100,000 z<3 quasars in DR1 (95% efficient to g=21) SDSS is 85% efficient to g=19 1,000,000 quasars from 0<z<5 in the whole SDSS area. Richards et al. 2004c

88. The M BH -sigma Relation ( Tremaine et al. 2002; also Ferrarese & Merritt 2000; Gebhardt et al. 2000; Magorrian et al. 1998) Massive black holes co-evolve with their host galaxies.