AGN and Quasar Clustering at z= : Results from the DEEP2 + AEGIS Surveys Alison Coil Hubble Fellow University of Arizona Chandra Science Workshop November 2006

Main Points 1.Different QSO/AGN formation theories predict different clustering with luminosity (host and/or QSO) and host color - clustering constrains models 2.QSOs cluster more like blue than red galaxies at z~1 (seen using local overdensity measure as well) 3.Chandra AGN cluster more like red than blue galaxies at z~0-1, see evidence for luminosity- dependence in clustering as well 4. Cross-correlating with large samples of galaxies rather than using QSO/AGN samples alone gives smaller errors - both Poisson and cosmic variance

DEEP2: A Redshift Survey at z=1: DEEP2 is a completed redshift survey using the Keck II telescope, covering multiple fields on the sky (for cosmic variance) to study galaxy evolution and LSS at z= One field is the Extended Groth Strip (EGS), which has 8 Chandra pointings of 200 ks each over a 2 x 0.25 degree field, with redshifts from z= in this field to R AB =24.1. DEEP2 has a high sampling rate (60%) and precise redshifts (<70 km/s) - good for clustering and environments. Full sample has 50,000 galaxies over 3 sq. degrees. EGS has 10,000 galaxies in 0.5 sq. degrees. (Chandra team: K. Nandra, A. Georgakakis, E. Laird)

AEGIS: the All-wavelength Extended Groth Strip International Survey Spitzer MIPS, IRAC DEEP2 spectra and K s imaging HST/ACS V,I (Cycle 13) Background: 2 x 2 deg from POSS DEEP2/CFHT B,R,I GALEX NUV+FUV Chandra & XMM: Past coverage Chandra (1.6Ms) VLA - 6cm + 21cm SCUBA

Clustering Primer Trace different physics on different scales: Smallest scales (r < 100 kpc/h): mergers + galaxy-galaxy interactions Intermediate scales (100 kpc/h < r < 2 Mpc/h): radial profiles of galaxies w/in halos / groups and clusters Large scales (r > 2 Mpc/h): large-scale density field / cosmology / host dark matter halo mass For a given cosmology: Estimate dark matter host halo mass - cosmological context, compare with simulations Can trace same galaxies (evolving populations) at different redshifts - allows you to connect different surveys and z’s Constrain galaxy and AGN formation and evolution models

SDSS QSOs in DEEP2 fields 36 SDSS + 16 DEEP2 spectroscopic QSOs in the DEEP2 fields between z= :

Clustering of Galaxies around QSOs Clustering of DEEP2 galaxies around SDSS QSOs at z= Errors include Poisson errors + cosmic variance. Similar errors as surveys with 1000s of QSOs (eg. 2dF) through use of cross-correlation with 10,000s of galaxies. Why measure the cross- correlation? Divide by the clustering of DEEP2 galaxies around DEEP2 galaxies to get the bias of QSO hosts… Coil et al ApJ

Relative bias of QSOs to DEEP2 galaxies The relative bias is 0.9 +/-0.2 Galaxies that host QSOs at z=1 have the same clustering properties (same halo mass) as typical DEEP2 galaxies. Not as clustered as red galaxies - more like blue galaxies (2  ). See using local overdensity / environment measures as well. Constrains host type for QSOs and QSO lifetimes! No dependence is seen on magnitude or redshift or scale. Coil et al ApJ

Clustering of X-ray AGN in AEGIS EGS is 0.5 deg 2 : 2 o x0.25 o transverse scales: ~46x6 Mpc/h at z=0.5 ~80x10 Mpc/h at z=1 z= have ~10,000 galaxies and (so far) ~200 Chandra sources with z’s to use to measure cross- correlation with galaxies as a function of both color and magnitude X-ray AGN are seen to be red or blue+massive MBMB color red Nandra et al ApJL

Now: Clustering of AGN in AEGIS First results: -no apparent difference with redshift (0.2<z<0.7 and 0.7<z<1.5) -significant dependence with luminosity: optically brighter AGN (-20.5>M B >-23) are ~50% more biased/clustered than fainter AGN (-17.5>M B >-20.5) -X-ray AGN cluster more like red than blue galaxies overall -cluster more than QSOs! -redder X-ray AGN cluster more than bluer AGN Coil et al. in prep

Environments of Chandra sources local overdensity magnitude Using local overdensity of nearby galaxies (in 3D - use 3rd nearest neighbor): brighter galaxies are in more overdense regions than faint galaxies and red galaxies in overdense regions as well. X-ray AGN are in overdense regions, like red and/or bright galaxies - reside in massive hosts at z~1. Georgakakis et al. in prep color

QSO/AGN Formation and Evolution Competing QSO/AGN formation/evolution models predict different clustering properties, through assumed accretion and lifetimes: - all begin with major mergers 1. Kauffmann and Haenelt 1. Kauffmann and Haenelt predict a strong luminosity-dependence to AGN clustering, based on assumed lightcurve and gas mass accreted ~ host halo mass, leads to luminosity~halo mass - doesn’t fit the data! 2. Lidz, Hopkins et al. 2. Lidz, Hopkins et al. predict less luminosity-dependence, as the light curve is not exponential, bright/faint QSOs are similar objects - but their typical host halo masses at z~1 higher than we find 3. Croton et al. in good agreement with our results. 3. Croton et al. follow the Kauffmann and Haenelt model, but include ‘radio mode’ for AGN, where galaxies in halos above a threshold mass can not accrete gas - shuts off SF and black hole accretion. Predict blue galaxies have QSOs at z~1 and fainter AGN in red galaxies - in good agreement with our results.

Final Points Our results favor galaxies undergoing a QSO phase before settling on the red sequence with a lower luminosity AGN. Measuring QSO/AGN clustering in fields with galaxy redshifts allows cross-correlation (small scales and with low errors) and local environment measures. Can also compare with red and blue galaxies at the same redshift and in the same volume. To not be dominated by cosmic variance you need wide areas (few degrees) and multiple fields. However, in comparisons to galaxies in the same volume (cross-correlations, environment) cosmic variance roughly cancels. Clustering of QSOs/AGN constrains lifetimes, host halo mass, host galaxy type and differentiates between formation models, especially if have wide luminosity range. Caveat: have to know if you’re seeing all QSOs/AGN or if there are sample biases.