Early Results from the DEEP2 Redshift Survey

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Early Results from the DEEP2 Redshift Survey
Venezia, Oct. 2003

• Jeffrey Newman
• and the DEEP2 Team

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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

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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.

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Early results and current work include

• Spectroscopic and color classification of
  galaxies at z1
• 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. Koos talk)
• All results presented here are based on 3-10 of
  the expected DEEP2 sample (now gt1/3 observations
  done)

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Principal Component Analysis (PCA)
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 OII 3727.

Madgwick et al. 2003 astro-ph/0305587
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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.
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Galaxy colors can also be used for
classification...
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 z0, the
distinction between early and late types is
readily apparent.
Weiner et al. 2003, Willmer et al. 2003
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Clustering in DEEP2 First Redshift Maps
Projected maps of two DEEP2 pointings (of 13
total). Red early-type (from PCA).
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Two-point correlations x(rp,p)
entire redshift range
two redshift sub-samples
line-of-sight separation
transverse separation
lt1 pointing, 5 of final sample
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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 dPn dV
(1x(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 r05 Mpc/h and g1.8. 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!
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Real Space vs. Redshift Space

• Peculiar velocities distort our maps
• czH0 d vp
• fingers of God on small scales
• coherent infall of galaxies on large scales

redshift space
real space
redshift space
real space
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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-do
minated 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
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Clustering as a function of Color and Spectral
Type
Red galaxies dashed lines Blue galaxies solid
lines
Redder galaxies have a larger correlation length
and larger velocity dispersion, as do
absorption-line galaxies reside in more
clustered / dense environments.
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Clustering in Color and Spectral Type samples
Redder galaxies have a larger correlation length
and a steeper slope than bluer galaxies B-Rgt0.7
r0 4.32 (0.73) g1.84 (0.07) B-Rlt0.7 r0 2.81
(0.48) g1.52 (0.06) Absorption-dominated
galaxies have a larger correlation length and
shallower slope than emission-line
galaxies Absorption r0 6.61 (1.12) g1.48
(0.06) Emission r0 3.17 (0.54) g1.68 (0.07)
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Galaxy bias
Galaxy bias b ratio of galaxy clustering
relative to the dark matter clustering
Not all structures cluster the same some must
be biased Observations at z0 show that the
galaxy bias can depend on scale, luminosity,
morphology, environment, color Bias is also
expected to evolve with z!
Galaxy formation simulation by Kauffmann et al.
greydark matter particles colorsgalaxies
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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).
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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
Voronoi partition in 2 dimensions
Gerke et al., Cooper et al., in prep
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Galaxy Groups and Clusters in DEEP2
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.
redabsorption-dominated
redpairs blueNgt2 size?log (?) ? log (halo
mass)
Gerke et al. 2004, in prep
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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
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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
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Luminosity-linewidth relations
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).
Weiner et al. 2004
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Velocity dispersions of satellite galaxies
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.
Conroy et al. 2004
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Conclusions

• DEEP2 is more than a third of the way to
  completetion, and theres 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!