What To Do With What To Do With 1,000,000 Quasars1,000,000 Quasars

Gordon RichardsGordon Richards

Drexel UniversityDrexel 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

Additional ReferencesAdditional References

Myers et al. 2007b, Clustering Analyses of 300,000 Photometrically Classified Quasars. II. The Excess on Very Small Scales, ApJ, 658, 99Myers et al. 2008, Quasar Clustering at 25 kpc from a Complete Sample of Binaries, ApJ, submittedHennawi 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,74f3520Giannantonio 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

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!99 year lease!

The Sloan Digital Sky Survey The Sloan Digital Sky Survey (SDSS) Quasar Sample(SDSS) Quasar Sample

Spectra of ~100,000 quasars in 10,000 sq. deg.Spectra of ~100,000 quasars in 10,000 sq. deg.

i < 19.1 for z<3.0i < 19.1 for z<3.0

i < 20.2 for z>3.0i < 20.2 for z>3.0

Both color and radio selectionBoth color and radio selection

See Richards et al. 2002, AJ, See Richards et al. 2002, AJ, 123, 2945 for details123, 2945 for details

So far: So far: ~77,000 quasars from z=0 to z=6.4. ~77,000 quasars from z=0 to z=6.4. See Schneider et al. 2007, arXiv:0704.0806See Schneider et al. 2007, arXiv:0704.0806

Quasar Quasar Surveys Surveys StatusStatus

Hasinger et al. 2005

Optimizing Quasar SurveysOptimizing Quasar SurveysX-ray/IR surveys are deep enough (up to a few 1000 X-ray/IR surveys are deep enough (up to a few 1000 AGN/sq. deg.), but not wide enough.AGN/sq. deg.), but not wide enough.

Optical surveys are wide enough, but not deep enough.Optical surveys are wide enough, but not deep enough.

SDSS

Need deeper optical surveys and/or larger area X-ray/IR surveys.Need deeper optical surveys and/or larger area X-ray/IR surveys.

.

Can We Do Better?Can We Do Better?

Yes, we can.

Why We Need To Why We Need To Do BetterDo Better

Merger Scenario w/ FeedbackMerger 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

How Can We Test This?How Can We Test This?

• The Quasar Luminosity Function• active lifetime• accretion rate• MBH distribution

• Quasar Clustering • L, z dependence• small scales

Quasar Luminosity FunctionQuasar Luminosity Function

Croom et al. 2004

Space Space density of density of quasars as a quasars as a function of function of redshift and redshift and luminosityluminosity

Typically fit Typically fit by double by double power-lawpower-law

Density EvolutionDensity Evolution

Number of Number of quasars is quasars is changing as changing as a function of a function of time.time.

Luminosity EvolutionLuminosity Evolution

Space density Space density of quasars is of quasars is constant. constant. Brightness of Brightness of individual individual quasars is quasars is changing.changing.

Hopkins et al. 2005Hopkins et al. 2005Most 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).

The SDSS QLFThe SDSS QLFSDSS, 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

Understanding the High-z QLFUnderstanding the High-z QLFThe 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

We Can Do BetterWe Can Do Better

Hopkins, Richards, & Hernquist 2007

The Future: Efficient Target The Future: Efficient Target Selection + Photo-z’sSelection + 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.

M

F

A

redblue

z > 3 quasars

red

blue

z < 2.2 quasars

Traditional Quasar Selection

Spitzer-IRAC (Mid-IR) SelectionSpitzer-IRAC (Mid-IR) Selection

GAL

STAR

AGN

e.g. Lacy et al. 2004, Stern et al. 2005

How Can We Do Better?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

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

““NBC”: Bayes’ (1763) RuleNBC”: Bayes’ (1763) Rule

€

P(Star | x) =P(x | Star)P(Star)

P(x | Star)P(Star) + P(x | QSO)P(QSO)

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

““KDE”: Kernel Density EstimationKDE”: Kernel Density Estimation

€

PDF =1

NKh

i

N

∑ x − x i( )

€

Kh (z)∝ exp−z2

2h2

⎛

⎝ ⎜

⎞

⎠ ⎟

x

xi

But Naïve KDE is But Naïve KDE is OO((NN22))

Dual-tree MethodDual-tree Method

• Tree building is Tree building is OO((NN log log NN); usually fast in comparison ); usually fast in comparison to the rest of computationto the rest of computation

• Classification of 500k objects in ~900 sec for Classification of 500k objects in ~900 sec for reasonable bandwidthsreasonable bandwidths

• See Gray, Riegel in Compstat 2006See Gray, Riegel in Compstat 2006

Separating Quasars from StarsSeparating Quasars from Stars

840,000 – 1,060,000 quasars

DR6 ResultsDR6 Resultsincluding high-zincluding high-z

Richards et al. 2008

Quasar Photo-zQuasar Photo-z

u g r i z

z=1.5

Photometric RedshiftsPhotometric Redshifts

Richards et al. 2001

Weinstein, Richards et al. 2004

Photometric redshifts are 80% accurate to within 0.3

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

SDSS+UKIDSS+IRACSDSS+UKIDSS+IRACz=1.5

Hα plus slope change makes for robust photo-z

LSSTLSST

LSST corp.

QSO Detection With TimeQSO Detection With Time

1955-1990: Slow! Methods include

radio/UVX detectionA. Myers

QSO Detection With TimeQSO Detection With Time

1990-2000: Multi-Fiber Spectrographs/Plate Scanning Machines

QSO Detection With TimeQSO Detection With Time

1995-2002: Long-term Surveys 5 years yields

~80,000 QSOs

QSO Detection With TimeQSO Detection With Time

2002-2010: Million+ QSOs via Photometric

Classification?

Autocorrelation Function Autocorrelation Function (())• Red Points are, on Red Points are, on

average, randomly average, randomly distributed, black points distributed, black points are clusteredare clustered

• Red points: Red points: (()=0)=0• Black points: Black points: (()>0)>0• Can vary as a function of, Can vary as a function of,

e.g., angular distance, e.g., angular distance, (blue circles)(blue circles)

• Red: Red: (()=0 on all scales)=0 on all scales• Black: Black: (() is larger on ) is larger on

smaller scalessmaller scalesA. Myers

•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%

•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).

• Use ellipsoidal collapse model (Sheth, Mo &

Tormen, 2001, MNRAS, 323, 1) to turn

estimates of bQ into 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).

Hopkins+05 ApJ, 630, 716 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.”

Lidz et al. 2006Lidz 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

Luminosity EvolutionLuminosity Evolution

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

bias

Mg

Luminosity EvolutionLuminosity Evolution

• Similarly from the Similarly from the SDSS+2dF=2SLAQ SDSS+2dF=2SLAQ quasar survey. quasar survey.

da Angela et al. 2008

What We What We (Used To) (Used To) ExpectExpect

1. Galaxies (and their DM halos) grow through hierarchical mergers

2. Quasars inhabit rarer high-density peaks3. If quasars long lived, their BHs grow with cosmic time4. MBH-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

What We GetWhat We Get1. 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. MBH-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.

ConclusionsConclusions

• 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!

Additional ReferencesAdditional References

Myers et al. 2007b, Clustering Analyses of 300,000 Photometrically Classified Quasars. II. The Excess on Very Small Scales, ApJ, 658, 99Myers et al. 2008, Quasar Clustering at 25 kpc from a Complete Sample of Binaries, ApJ, submittedHennawi 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,74f3520Giannantonio 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