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AGN Surveys and Luminosity Function

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					Lecture 2: AGN Survey and Luminosity
              Function

              Xiaohui Fan
        AGN Summer School, USTC
              May 25, 2007




                Background: 46,420 Quasars from the SDSS Data Release Three
                   Goal
• Derive the density of AGNs as function of
  bolometric luminosity, redshift
   – (Lbol, z, type)
• Relates to:
   – Characterizing accretion history:
      • Distribution functions of black hole
        activity as function of MBH, accrection
        rate and radiative efficiency and
        redshift
   – Probing galaxy/BH coevolution
   – Test unification model
                Basic Issues
• Instead of (Lbol, z, type), we observe:
   – N(f, z, AGN type, selection criteria)
   – Selection effect
       • Incompleteness due to selection criteria
         (correctable)
       • Selection bias (e.g., optical survey missing obscured
         sources)
   – Bolometric correction
   – Redshift effect
       • Flux-limited vs. volume limited, truncated data set
       • Limited luminosity range at any given redshift,
         parametric vs. non-parametric
       • K-correction
                    Outline
1. AGN surveys
2. LF parameterization and selection effects
3. Evolution of optical AGN LFs
   •   Density vs. luminosity evolution
   •   Downsizing
4. Putting things together:
   •   Soltan argument and constraints of BH accretion
       properties
5. Quasar Clustering
                    References
• Textbook:
   – Peterson Chaps 10 and 11
• Recent Review
   – Osmer, astro-ph/0304150
• Optical
   – Richards et al. 2006, AJ, 131, 2766
• X-ray
   – Brandt and Hasinger, 2005, ARAA, 43, 827
• Luminosity function methodology
   – Fan et al. 2001, AJ, 121, 31
• Luminosity function across wavelength
   – Hopkins et al. 2007, ApJ, 654, 731
• Soltan argument
   – Yu and Tremaine 2002, MRNAS, 335, 965
   Observational Properties of AGNs
• Textbook definition
   –   Small angular sizes (compact)
   –   Cosmological distance
   –   High luminosity?
   –   Broad-band continuum emission
   –   Emission Lines indicative of hard ionizing source
   –   Variability
   –   Polarization (subset)
• AGN surveys utilize one or more of these
  properties
                 How to find AGNs
• High luminosity AGNs:
   –   LAGN >> Lgal
   –   AGN light dominates
   –   Point source in the wavelength observed
   –   Distinct SED
• Optical Color Selection
   – Sandage (1971)
   – 2dF (2000):
      • 400 deg2
      • 25000 quasars
  SDSS at Your Service




Courtesy of Arizona graduate students
                 SDSS Overview
• Primary Telescope: 2.5m
  wide-field (2.5 deg)
• Imaging Survey (wide-field
  54 CCD imager)
   – Main Survey: 10000 deg2
   – Five bands, 3000 – 10000 Å
   – rlim ~ 22.5, zlim ~ 20.5
• Spectroscopic Survey
   – 106 galaxies (r<17.8)
   – 105 quasars ( 0 < z < 6.5)
   – Interesting stars, radio/x-ray
     sources etc.
                       SDSS Color Selection
•   Color selection
    – Type-1 quasars                        Stellar locus
    – Low-z
       • UV-excess (UVX),
         Palomar-Green (PG), 2dF   quasar          Z=3
         etc.                                                               Z=4

       • Contaminants: brown
         dwarfs
    – High-z
       • Lyman break, SDSS,
         DPOSS, APM
       • Contaminants: late type
         stars, brown dwarfs                         Z=5


• >90% of known AGNs are
  color-selected
                                                            Richards et al. 2002
    Selection effect of color selection
•   z=2.5-3.0 gap
    – Quasars have similar
      colors to F stars
• Missing redder or
  reddened quasars
• Missing obscured/type-2
  objects
• Only sensitive to high
  level of activity, high
  AGN/host contrast
             Slitless Spectroscopy


                                       QuickTime™ an d a
                             TIFF (Uncompressed) decompressor
                                are need ed to see this picture.




• Identify broad emission line from prism plates
    – Large Bright Quasar Survey (LBQS)
    – Hamburg ESO Survey (HES)
    – Palomar Grism Transit Survey
• Selection Effect
    –   Strong redshift dependence
    –   Biases towards strong emission line
    –   Mostly on photographic plates, difficult to calibrate
    –   Problem with crowded field
           X-ray Surveys
                                   Brandt and Hasinger 2005




• X-ray sky is dominated by AGNs
• X-ray selection sensitive to both type-1 and modestly obscured
  type-2 sources
• Chandra/XMM deep fields capable of reaching very low
  luminosity
• Host galaxy not an issue until ~10-5~-6 Eddington luminosity
         Other Selection Methods
• Radio
    –   Where everything started (Schmidt 1963)
    –   ~10% quasars are radio-loud
    –   FIRST and NVSS surveys
    –   Does radio-loud quasars evolve the same way as radio-quiet ones?
• Near-IR selection
    – KX (K-band excess) method
    – Less affected by reddening
• mid-IR selection
    – Dust emission peaks at rest-frame 10-50 microns
    – Select both type 1 and type 2
    – Can select Compton-thick sources
• Variability
• Proper motion survey
• Serendipity (Spinrad method)
Quest to the Highest Redshift Quasars   IR survey
                                        (UKIDSS,
                                        VISTA,




                               SDSS
                        •               LBT)




                     APM
                     CCD
        Radio
         So how far could each of
          these techniques go?
• Lyman break:
   – Quasars: 6.4
   – Galaxies: 7-8
• Slitless spectroscopy
   – Quasars: 4.7
   – Galaxies: 5.5
• multiwavelength
   –   Quasars: 5.2 (X-ray), maybe 7?
   –   Quasar: 5.8 (IR)
   –   Quasar: 6.1 (radio)
   –   Galaxies: 5.2 (radio)
• Variability:
   – Quasar: 4.5
• Luck:
   – Quasars: 4.3
   – Galaxies: 5.8
   Surveys of low-luminosity AGNs
• Low-luminosity type 1 and type 2 sources in X-
  ray samples
• Emission-line selected sources in galaxy redshift
  surveys:
   – Optical wavelength: LAGN< L host
   – Spectra dominated by host galaxy; stellar/ISM
     component
   – CfA redshift survey sample (1980s)
   – Ho, Filippenko and Sargent (1997) sample: high S/N
     spectra of 486 nearby galaxies; half shows AGN
     signatures
   – SDSS selection: Hao et al., Kauffmann et al., Greene et
     al., Zakmaska et al. (excellent Ph . D. theses!!)
        Selection of low-luminosity AGNs
• Stellar spectra subtraction
   – Best-fit templates constructed from Principle   Hao et al.
     Component Analysis




• Bladwin-Phillips-Telrivich Digram
   – Separating AGNs from starbursts




                           Kauffmann et al.
        Two extremes from galaxy
                 surveys
• The smallest broad-line AGNs (Greene, Ho, Barth)




      Greene et al.
The most luminous type-2 quasars




                  Zakamska et al.
                    Outline
1. AGN surveys
2. LF parameterization
3. Evolution of optical and X-ray selected AGNs
   •   Density vs. luminosity evolution
   •   Downsizing
   •   The highest redshift quasars
4. Putting things together:
   •   Soltan argument and constraints of BH accretion
       properties
5. Quasar Clustering
                         46,420 Quasars from the SDSS Data Release Three
           5
                Ly forest

           3
                     Ly
           2
                               CIV
redshift




                                       CIII
                                                         MgII

                                                                FeII
           1
                                     FeII


                                                                       OIII      H
           0
               4000 A                       wavelength                        9000 A
M-z distribution from SDSS




                    Richards et al. 2006
     Luminosity Functions:
     1/VA Estimator
     (non-parametric)                              Object Too Faint
 Given a single object, X, visible
 within some volume, VA
                                                          Object
                                                         Detectable
           1             1
     nX     
          VA V z m ax   V  z m in 

For a number of objects i: i : L  Li  L  dL
                                                            Too
                                                           Bright
 ˆ L     1
          V
                      This 1/VA estimator is a
 X                   maximum likelihood estimator
          i  A ,i

                      Issue: Binning; selection effcts
Parameterization




                   SIMPLE POINTS:
                   • There is no
                   difference in PDE vs.
                   PLE for power-law
                   LF;
                   • But LF will
                   eventually turn over
                   for the total number
                   to converge;
                   • The real LF is likely
                   more complex
                   Parameterization
     • Quasar LF: double power-law
                                    *
                 (L) 
                        (L /L* )  h  (L /L* )  l

                               '*
        (M)  0.4[M M * ][ 1]      0.4[M M * ][ l 1]
            10             h
                                   10
     • Luminosity-dependent density evolution (Schmidt
       and Green 1983):
               (L,z) = (L,z) (L,z=0)
                 overall density evolves;
                 Shape (bright and faint end slopes) evolves as well
          Selection Function
     Example: optical color selection
• Color of quasar is a function of:
   – Redshift
   – Spectral property:
      • Continuum slope
      • Emission line strength
      • For high-z : random distribution of absorption
        systems along line of sight
   – Luminosity: error distribution in the survey
f~-




         XF et al. 2001
      Model selection function
• Construct model quasar color sets that includes
  realistic distributions of quasar spectral properties
  and observed error distributions, then run selection
  algorithm on model data set
   – -> p(L,z,SED)
• Limitations
   – Accuracy relies on assumptions on spectral property
     distributions (which sometimes is derived from the
     same survey)
   – Can never correct for objects that survey is insensitive
     to: optical: obscured sources, very red quasars etc.
   – Correction is large (and sensitive) in some cases (e.g.
     optical: z~2.8
Richards et al. 2006
                    Outline
1. AGN surveys
2. LF parameterization
3. Evolution of optical and X-ray selected AGNs
   •   Density vs. luminosity evolution
   •   Downsizing
   •   The highest redshift quasars
4. Putting things together:
   •   Soltan argument and constraints of BH accretion
       properties
5. Quasar Clustering
Luminosity Function from
   2dF Quasar Survey




                     Boyle et al. 2001
          Luminosity function from 2QZ
• Best fit model: pure luminosity evolution:




   : cosmic look-back time; L*() ~ exp(6)
    ~ 6;  ~ -3.3;  ~ -1.0
                                 or L(z) ~ exp(6)
• However…
   • M* constant apparent mag
      • Selection effect??
   • Faint end slope poorly determined
      • From 2001 to 2004 papers




                     Croom et al. 2004
What’s the Faint End Slope of QLF?

                           Faint slope measurement
                     z=0
                           Ranges from -1.o to -2.0…
                           lead to large uncertainties in
                           in the total luminosity and
                           mass density of quasar pop.




   Hao et al. 2004
SDSS quasar LF




                 Richards et al. 2006
                SDSS quasar LF




                                                 Richards et al. 2006
• Strong evolution in bright end slope at z>3
   – Can’t be luminosity evolution all the way
• But doesn’t go faint enough at low-z to differentiate PLE
  from PDE or else
   density evolution of luminous quasars
                                         Density of quasars

                SFR of galaxies




                        Bouwens et al.

Exponential decline of quasar
density at high redshift, different
from normal galaxies                         Richards et al. 2006,
                                                Fan e al. 2005
                 X-ray AGN LF




• Result 1: Downsizing of AGN activity
   – Quasar density peaks at z~2-3
   – AGN density peaks at z~0.5 - 1
   – Paradox 1:
      • Most of BH accretion happens in quasars at high-z
      • Most of X-ray background in Seyfert 2s at low-z
                     X-ray LF




• Result 2:                                      Miyaji et al. 2006
   – PLE doesn’t work; need luminosity-dependent density evolution
     to characterize evolution of the entire LF
                        X-ray LF




• Result 3:
    – Type 2 fraction a strong function of luminosity
    – Paradox 2:
       • At high (quasar) luminosity: type 2 <20%; optical color
          selection is highly complete since all are type 1s, and includes
          most of luminosity AGN population emitted in the Universe
       • At low (Seyfert) luminosity: type 2 ~80%; optical color
          selection miss most of the AGNs in the Universe in terms of
          number
                    Outline
1. AGN surveys
2. LF parameterization
3. Evolution of optical and X-ray selected AGNs
   •   Density vs. luminosity evolution
   •   Downsizing
   •   The highest redshift quasars
4. Putting things together:
   •   Soltan argument and constraints of BH accretion
       properties
5. Quasar Clustering
      Putting things together:
     Evolution of bolometric LF
• Hopkins et al. (2007):
   – Combines QLFs in optical, X-ray and IR
   – Over z=0-6 and the whole L range
   – Accounting for distribution of absorbing column and
     luminosity-dependent SEDs
   – Findings:
       • PLE doesn’t work
       • Both bright and faint-end slope evolve with z
       • Luminosity-dependent density evolution provides
         good fit for all data
Downsizing in all bands
General Evolutionary Trends




• And a calculator: www.cfa.harvard.edu/~phopkins/Site/qlf.html
    http://www.cfa.harvard.edu/~phopkins/Site/qlf.html
    ..
  Putting things together: Soltan’s argument

  • Soltan’s argument: QSO luminosity function (L,t) traces the accretion
    history of local remnant BHs (Soltan 1982), if BH grows radiatively



                                      t0                (1   ) Lbol
    0
            MnM ( M , t 0 ) dM    
                                   0
                                            dt 
                                               0
                                                       dL
                                                              c 2
                                                                       ( L, t );
                 local                                   accreted
                   n ( M , t ) : local BH mass function,
                    M        0




                    ( L, t ) : QSO luminosity function,
                                                       (1   ) Lbol
                         : efficiency, M                             .
                                                           c   2




Total mass density accreted = total local BH mass density
           New estimates of BH mass densities


•   Total local BH mass density:

     – local BH mass function nM(M,t0):
         • SDSS early-type galaxy sample n(,t0) (Bernardi et al. 2001)
         • the tight M• – relation (Tremaine et al. 2002)
     – •,local=(2.50.4)105 M/Mpc3 (h=0.65) (Yu & Tremaine 2002)



•   BH mass density accreted due to optically bright QSO phases:

     – (L,t): 2dF QSO Redshift survey (Boyle et al. 2000)
     – •,acc=2.1105[0.1(1- ) /] M/Mpc3 (Yu & Tremaine 2002)

                      ,local   ,acc if   0.1
•   Bright quasar phase can account for most of the BH mass growth; low efficiency
    accretion and obscured AGN not very important
The history of BH mass density accreted
          during quasar phase




                          Yu and Tremaine 2002
Expanding Soltan’s
    Argument




 Fitting QLF with local BHMF
                    Outline
1. AGN surveys
2. LF parameterization
3. Evolution of optical and X-ray selected AGNs
   •   Density vs. luminosity evolution
   •   Downsizing
   •   The highest redshift quasars
4. Putting things together:
   •   Soltan argument and constraints of BH accretion
       properties
5. Quasar Clustering
Galaxies are strongly clustered




            QuickTime™ and a
   TIFF (Uncompressed) decompressor
      are needed to see this picture.
           How about quasars?

                                SDSS




         2dF
Difficulty:
Quasars are rare!
Very large survey needed
quasars are as strongly
clustered as galaxies
Idea of biased galaxy formation
        Idea of biased galaxy/quasar
                  formation




• Bias: the relative strength of clustering between galaxy
  (quasar) and underlying dark matter
• Biasing is unavoidable for rare, high-z systems
• Bias factor (clustering strength) is a strong function of the
  mass of dark matter halo that hosts galaxy (quasar) as well
  as redshift
• For a given cosmology: clustering strength constrains
  dark matter halo mass and its evolution
        Clustering of Quasars
• What does quasar clustering tell us?
   – Correlation function of quasars vs. of dark matter
   – Bias factor of quasars  average DM halo mass
   – Clustering probably provides the most effective
     probe to the statistical properties of quasar host
     galaxies at high-redshift
   – Combining with quasar density  quasar lifetime
     and duty cycle
        Evolution of Quasar Clustering
• SDSS quasar survey
  – Clustering strength strong func.
    of redshift
  – Quasar lifetime ~10-100Myrs                      z>3.5


  – Quasars reside in 2-6x1012h-1Msun
                                         z=2.9-3.5
    DM halos




                                        Shen et al. 2007
                      Summary
• AGN Surveys
   – All selection methods suffer from selection effect which needs to
     be taken into account carefully
   – Optical surveys, esp. color selection are biased against obscured,
     reddened quasars and have low completeness at z=2.5-3.0
• AGN Luminosity Function
   – AGN density is strong function of redshift, and peaks at z~2
   – AGN LF is double power-law, with slopes also strong function of
     redshift
   – Luminosity-dependent density evolution best describes all data
   – Local BH density can be accounted for by accretion in quasar
     phase using Soltan’s argument
• AGN clustering
   – AGN are strongly clustered and strongly biased
   – Quasar clustering increases with redshift
   – Quasar clustering consistent with 107 yr lifetime and 1012-13 Msun
     halo mass

				
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posted:9/6/2011
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