Transcript AGN Surveys and Luminosity Function

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
– Low-z
• UV-excess (UVX),
Palomar-Green (PG), 2dF
etc.
• Contaminants: brown
dwarfs
– High-z
• Lyman break, SDSS,
DPOSS, APM
• Contaminants: late type
stars, brown dwarfs
Stellar locus
quasar
Z=3
Z=4
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™ and a
TIFF (Uncompressed) decompressor
are needed 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)
Radio
APM
CCD
•
SDSS
Quest to the Highest Redshift Quasars
IR survey
(UKIDSS,
VISTA,
LBT)
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 Xray 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
Component Analysis
• Bladwin-Phillips-Telrivich Digram
– Separating AGNs from starbursts
Kauffmann et al.
Hao 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
redshift
Ly
2
CIV
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
1
1
nX 

VA V zmax   V zmin 
For a number of objects i:
1
ˆ
 X L   
i V A,i
i : L  Li  L  dL
This 1/VA estimator is a
maximum likelihood estimator
Issue: Binning; selection effcts
Object
Detectable
Too
Bright
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]
h
10
 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
• However…
or L(z) ~ exp(6)
• 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?
z=0
Faint slope measurement
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


0
MnM ( M , t 0)dM 

t0
0
dt 

0
local
(1   ) Lbol
dL
 ( L, t );
2
c
accreted
n ( M , t ) : local BH mass function,
M
0
 ( L, t ) : QSO luminosity function,
 : efficiency, M 
(1   ) Lbol
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
– Quasars reside in 2-6x1012h-1Msun
DM halos
z>3.5
z=2.9-3.5
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