venice oct03

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Information about venice oct03

Published on August 29, 2007

Author: Breezy


Early Results from the DEEP2 Redshift Survey:  Early Results from the DEEP2 Redshift Survey Jeffrey Newman and the DEEP2 Team Venezia, Oct. 2003 The DEEP2 Collaboration:  The DEEP2 Collaboration Team Members: U.C. Berkeley: M. Davis (PI), A. Coil, M. Cooper, B. Gerke, R. Yan, C. Conroy U.C. Santa Cruz: S. Faber (Co-PI), D. Koo, P. Guhathakurta, D. Phillips, C. Willmer, B. Weiner, R. Schiavon, K. Noeske, A. Metevier, L. Lin, N. Konidaris, G. Graves Caltech: R. Ellis, C. Steidel, C. Conselice, K. Bundy U. Hawaii: N. Kaiser, G. Luppino LBNL: J. Newman, D. Madgwick U. Pitt.: A. Connolly JPL: P. Eisenhardt Princeton: D. Finkbeiner Keck: G. Wirth UCLA: T. Treu Update on Marc Davis...:  Update on Marc Davis... Marc suffered a stroke in late June; his recovery and rehabilitation is ongoing, at his home. He is now visiting campus, attending team meetings, reading email, etc. His participation increases every week, but the top priority for now remains rehab. Early results and current work include::  Early results and current work include: Spectroscopic and color classification of galaxies at z~1 The dependence of clustering () on galaxy properties The dependence of galaxy properties on environment Detection and membership determination for clusters and groups of galaxies Luminosity function evolution Kinematics of galaxies Studies of red galaxies (cf. D. Koo’s talk) All results presented here are based on 3-10% of the expected DEEP2 sample (now andgt;1/3 observations done) Principal Component Analysis (PCA):  Principal Component Analysis (PCA) Madgwick et al. 2003 – astro-ph/0305587 PCA allows us to define a minimum set of eigenspectra that span most of the variance in our sample. The most influential component primarily quantifies the strength of O[II] 3727. PCA for classification:  PCA for classification The strength of the first PCA eigenvalue alone provides an effective means for determining spectral types of galaxies, as seen in the stacked spectra of galaxies split according to this value. Galaxy colors can also be used for classification...:  Galaxy colors can also be used for classification... Weiner et al. 2003, Willmer et al. 2003 PCA allows us to classify galaxies based upon their spectra; however, we can also use our BRI photometry, along with redshift, to derive rest-frame broadband colors. Like at z~0, the distinction between early and late types is readily apparent. Clustering in DEEP2: First Redshift Maps:  Clustering in DEEP2: First Redshift Maps Projected maps of two DEEP2 pointings (of 13 total). Red = early-type (from PCA). Two-point correlations: x(rp,p):  Two-point correlations: x(rp,p) transverse separation line-of-sight separation andlt;1 pointing, ~5% of final sample entire redshift range two redshift sub-samples 2-point correlation function: x(r):  2-point correlation function: x(r) x(r) measures the excess probability above random of finding a galaxy in a volume dV at a distance of r from a randomly chosen galaxy: dP=n dV (1+x(r) ) where n is the mean number density of galaxies. x(r) measures the clustering in the galaxy distribution. x(r) is known to follow a power-law prescription locally: x(r) = (r0/r)g with r0~5 Mpc/h and g~1.8. r0 = scale where the probability of finding a galaxy pair is 2x random In the DEEP2 survey we measure galaxy clustering as a function of redshift, color, spectral type and luminosity! Real Space vs. Redshift Space:  Real Space vs. Redshift Space Peculiar velocities distort our maps: cz=H0 d + vp 'fingers of God' on small scales coherent infall of galaxies on large scales real space redshift space real space redshift space Projected correlation function:  Projected correlation function Summing x(rp,p) along line-of-sight yields wp(rp); can recover the real-space correlation fctn. if assume x(r)= (r0/r)g Redder/absorption-dominated galaxies exhibit much stronger correlations, as also is seen at lower redshifts. The difference in clustering strength is significant even with r0/g covariance. Errors are estimated using mock catalogs (Yan et al. 2003) - currently dominated by cosmic variance. The DEEP2 sample as a whole is not strongly biased compared to the dark matter: b ~ 1+/- 0.2 Coil et al. 2003, astro-ph/0305586 Clustering as a function of Color and Spectral Type:  Clustering as a function of Color and Spectral Type Redder galaxies have a larger correlation length and larger velocity dispersion, as do absorption-line galaxies: reside in more clustered / dense environments. Red galaxies: dashed lines Blue galaxies: solid lines Clustering in Color and Spectral Type samples:  Clustering in Color and Spectral Type samples Redder galaxies have a larger correlation length and a steeper slope than bluer galaxies: B-Randgt;0.7: r0= 4.32 (0.73) g=1.84 (0.07) B-Randlt;0.7: r0= 2.81 (0.48) g=1.52 (0.06) Absorption-dominated galaxies have a larger correlation length and shallower slope than emission-line galaxies: Absorption: r0= 6.61 (1.12) g=1.48 (0.06) Emission: r0= 3.17 (0.54) g=1.68 (0.07) Galaxy bias:  Galaxy bias Not all structures cluster the same – some must be biased Observations at z=0 show that the galaxy bias can depend on scale, luminosity, morphology, environment, color Bias is also expected to evolve with z! Galaxy bias b = ratio of galaxy clustering relative to the dark matter clustering Galaxy formation simulation by Kauffmann et al. grey=dark matter particles colors=galaxies Galaxy Clustering Results:  Galaxy Clustering Results The DEEP2 sample as a whole does not seem to be strongly biased compared to the dark matter: b ~ 1+/- 0.2 depending on assumed cosmology (especially s8). Any detailed comparisons to other (e.g. low-z) samples require accounting for differences in selection; most DEEP2 galaxies are blue (due to restframe-U selection) and sub-L*. Details may be found in: Coil et al. 2003, astro-ph/0305586 We also are studying angular correlations in the DEEP2 fields using our BRI photometry; that work is nearly complete (Coil et al. 2003b). Dependence of galaxy properties on environment:  Dependence of galaxy properties on environment The Voronoi volume of a galaxy is the amount of space that is closer to that galaxy than any other; it provides a parameter-free measure of the inverse number density of galaxies about any object (cf. Marinoni et al. 2002). High z resolution is required. We can use this measure to study how galaxy properties such as LF, color, spectral type, and linewidth vary with environment in the DEEP2 sample (and compare with local surveys). For instance, PCA emission-line galaxies are preferentially found in low-density regions: Gerke et al., Cooper et al., in prep Voronoi partition in 2 dimensions Galaxy Groups and Clusters in DEEP2:  Galaxy Groups and Clusters in DEEP2 Gerke et al. 2004, in prep red=pairs; blue=Nandgt;2; sizelog ()  log (halo mass) Voronoi-based methods can also be used to identify clusters and groups of galaxies (Marinoni et al. 2002). We are currently optimizing such techniques with mock catalogs, and have begun producing DEEP2 group catalogs. This will allow both the study of group property distributions and of group vs. field galaxies. red=absorption-dominated Luminosity Function evolution:  Luminosity Function evolution DEEP2 luminosity function measurements are well underway. Good agreement with COMBO-17 in range of overlap; also LF as a function of color, spectral type, etc. Willmer et al. 2003 Galaxy kinematics in DEEP2:  Galaxy kinematics in DEEP2 The high resolution used for DEEP2 observations yields well-resolved linewidths for all objects, and rotation curves as a free byproduct for thousands of objects. Shown are four 2d spectra exhibiting resolved, tilted [OII] emission and the derived circular velocity Vc(r). Cooper et al. 2004 Luminosity-linewidth relations:  Luminosity-linewidth relations Weiner et al. 2004 Since we can measure both luminosities and linewidths of DEEP2 galaxies, we can also explore the relationship between the two and compare to lower-z samples. Preliminary results suggest the T-F relation becomes brighter at higher redshift, in agreement with previous work (but with much larger samples). Velocity dispersions of satellite galaxies:  Velocity dispersions of satellite galaxies Conroy et al. 2004 We can explore the potential wells of galaxies at larger radii by examining the relative velocities of faint neighbors of bright galaxies (ala Prada et al. 2003). Preliminary tests on the data are promising, and we are testing our ability to reject interlopers with the mock catalogs of Yan et al. (2003). We should have a sample of hundreds of satellites by the end of DEEP2. Conclusions:  Conclusions DEEP2 is more than a third of the way to completetion, and there’s much science we can do with only the first 5-10% of the full sample. Many of these results will be enhanced by data from other wavebands in EGS: e.g. NIR T-F, star formation rate vs. spectral type vs. environment, comparisons of SFR diagnostics, clustering of starburst galaxies, etc. The next year should be very exciting!

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