17 may 04leonidas moustakas STScI 1 High redshift (z~4) galaxies & clustering Lexi Moustakas STScI

17 may 04leonidas moustakas STScI 2 credit Everybody at GOODS & ODT!  Soo Lee (JHU) (advisor: M. Giavalisco)  Paul Allen (MSO,  Emily MacDonald (Oxf) (advisor: G. Dalton)

17 may 04leonidas moustakas STScI 3 GOODS: Giavalisco et al 2004 Montage courtesy of F. Summers total GOODS: ~320 arcmin 2 see M. Giavalisco talk tomorrow!

17 may 04leonidas moustakas STScI 4 Finding high-z galaxies: z~4  The Lyman-dropout technique, B-V vs V-z (for z~4) -- multiwavelength is KEY  The space-based GOODS data use the z-band & are extremely deep compared to the ground -- ~2-3 mag fainter.  In total GOODS ACS area, ~2000 z~4 galaxies B-dropouts, z~4 Giavalisco et al. 2004

17 may 04leonidas moustakas STScI 5 LBG redshift distributions, from monte carlo simulations B V i The redshift distributions are well-constrained through simulations. The completeness is more difficult to pin down. (The B-drops are the z~4). Giavalisco & S. Lee 2004

17 may 04leonidas moustakas STScI 6 morphologies of faint z~4 galaxies  The sizes of star forming galaxies above z~1 are sub- arcsec (Ferguson et al 2004)  As shown here, the morphologies are varied and can be complex  The pair/group statistics are crucial for characterizing environment Viz 1'' from the v1.0 GOODS data Check out the scale!

17 may 04leonidas moustakas STScI 7 clustering of faint z~4 galaxies  With the angular correlation function measured directly, and a simulated N(z), we invert & calculate the spatial correlation function  (r) = (r/r 0 ) - , usually assumed to be a power- law on relatively large scales, with characteristic scale r 0. S. Lee et al. 2004, in prep. w(theta) vs angular separation } nb: many neighbors within 10-20arcsec!

17 may 04leonidas moustakas STScI 8 clustering with app. magnitude  Clustering measured in the GOODS data to different magnitude limits. (The error bars are smaller than the points!)  There is evidence for stronger clustering in the brighter samples... (See also Giavalisco & Dickinson 2001). GOODS data from S. Lee et al. 2004, in prep. spatial clustering vs limiting apparent magnitude

17 may 04leonidas moustakas STScI 9 clustering with abs. magnitude   Transform (approximately) to rest-frame B J magnitudes   The brightest point is sub-L *   What happens if one goes to much brighter absolute magnitudes??   => We don't know from GOODS! Area is not large enough to find very rare objects... spatial clustering vs absolute magnitude (approximate)

17 may 04leonidas moustakas STScI 10 The Oxford-Dartmouth Thirty- Degree (ODT) Survey MacDonald et al 2004, MNRAS, in press 5  limits completion vega to date U > 25 B 26.0 V 25.5 R >23 deg 2 i 24.5 Z 22 K 3.5 deg 2 MacDonald et al Moustakas et al in prep (K-band part) andr lynx herc virgo

17 may 04leonidas moustakas STScI 11 The ODT Survey: A wide-field multi- survey  The Andromeda field of the ODT Survey A GOODS Field MacDonald et al. 2004

17 may 04leonidas moustakas STScI 12 clustering of bright z~4 galaxies  Clustering measurements of B-drops in ODT Survey, from a ~2deg 2 subset Allen et al. 2004, MNRAS  N(z)'s 'realized', and angular correlation function inverted.  These LBG samples are bright, with i<24.5 (2 mag brighter than GOODS) Allen et al } nb: no neighbors within 10-20arcsec!

17 may 04leonidas moustakas STScI 13 L-dependent clustering at z~4 GOODS: S. Lee et al. ODT: P. Allen et al.

17 may 04leonidas moustakas STScI 14 L-dependent clustering at z~4 L * is around here GOODS: S. Lee et al. ODT: P. Allen et al.

17 may 04leonidas moustakas STScI 15 L-dependent clustering at z~0 z~0 GOODS: S. Lee et al. ODT: P. Allen et al. 2dF: Norberg et al. 2002

17 may 04leonidas moustakas STScI 16 cosmic variance in this result  Assuming simple galaxy-halo correlation  larger volumes = less cosmic variance  smaller clustering = less cosmic variance  We calculate a similar level of cosmic variance across the z~4 result --  GOODS: small volume but small clustering -> cv~20%  ODT-S: large volume but large clustering -> cv~40%  To bring the high-L variance down to 20%, need >10 times more area! But even that isn't enough.  Why is that? -- Onwards, to:

17 may 04leonidas moustakas STScI 17 beyond sweet peas  Clustering, (dark matter) masses, and environment  With analytic LCDM, we can connect the clustering to the minimum dark matter halo mass.  Combining the clustering with the space densities, a Halo Occupation Distribution (HOD) formalism can constrain the number of galaxies per halo vs halo-mass  Adding luminosity information to this, the Conditional Luminosity Function (CLF)  Let's quickly consider the Halo Occupation formalism

17 may 04leonidas moustakas STScI 18 dark matter halo masses Moustakas & Somerville 2002 There can be many galaxies in each dark matter "halo", or none. The average behavior can be parametrized with the Halo Occupation Function, or Distribution N(M>M min ) = (M/M 1 )  M min - threshold halo mass ** from clustering M 1 - 'typical' mass ** from clustering & density  - mass function slope ** from small-scale clustering! "bias" comes from the clustering, which fixes the 'minimum' DM halo mass space density bias

17 may 04leonidas moustakas STScI 19 galaxies' dark matter halos  Here we plot the results for z~0 ellipticals, z~1.2 EROs, and z~3 LBGs (LAM & Somerville '02)  The occupation function parameters can be constrained through the measured clustering strength and the space density  The SLOPE (a 'free' parameter in this plot), can be constrained by very small-scale statistics M&S02

17 may 04leonidas moustakas STScI 20 clustering evolution  The simplest model hasa galaxies following the dark matter they're associated with -- 'galaxy conserving model' (Fry 1996)  See the behavior of populations with properties established at different redshifts. Do they 'connect'? correlation scale linear bias   The different z~4 galaxies may have different histories & futures...

17 may 04leonidas moustakas STScI 21 Conclusions  There is evidence for luminosity- dependent clustering in galaxies, at z~4 as well as locally  Need 'complete' census at all scales => DEPTH  >10s of square degrees or more will be required to characterize this: => LARGE SOLID ANGLE  To constrain the SLOPE of the occupation function, we need very sub- few-arcsec pair/group info.: => HIGH SPATIAL RESOLUTION  A multi-wavelength SNAP/JDEM/LEGASY type mission would clean this up...