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Evolution of clustering in COSMOS: a progress report. Luigi Guzzo (INAF, Milano) N. Scoville, O. Le Fevre, A. Pollo, B. Meneux, & COSMOS Team.
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Evolution of clustering in COSMOS: a progress report Luigi Guzzo (INAF, Milano) N. Scoville,O. Le Fevre, A. Pollo, B. Meneux, & COSMOS Team
Basic goal of COSMOS: measure the interplay between the growth of large-scale structure and the formation and evolution of galaxy properties within it (morphology, SED, size, luminosity, presence of central black hole, …) Quantify local structure via moments of the galaxy distribution: mean density, two-point correlation function, higher-order moments Goal here: Measure two-point correlation function as a function of redshift from photo-z COSMOS catalogue Final goal: measure evolution of how clustering depends on galaxy properties
Redshift-space correlations simply destroyed by photo-z errors • Solution: treat redshift errors as (very large) redshift-space distortions and use projected function in z slices Usual effect of real to redshift-space mapping (using mock survey):
Destructive effect of the large photo-z errors on redshift-space correlation function:
Recover diluted signal by projecting x(rp,p) along the line of sight and fitting a power-law real-space correlation function x(r)=(r/r0)-g :
Application to May 2005 COSMOS photo-z catalogue: • IAB < 24 • 150.83 < RA < 149.4 & 1.5 < DEC < 2.9 • Use very thick redshift slices (>> photo-z error): • 0.2 – 0.6 40515 galaxies • 0.4 – 0.8 47222 galaxies • 0.6 – 1.0 50097 galaxies • 0.8 – 1.2 34480 galaxies • Compute x(rp,p) and project power back onto wp(rp) • Fit power-law x(r) and recover amplitude and slope at each redshift
Remarks and perspectives: • Encouraging result: the catalogue has improved! The recovered evolution of the correlation length r0 and slope g of x(r) is fully consistent with what is measured in the VVDS spectroscopic survey (Le Fevre et al. astro-ph/0409135) • Remaining problems: stars with wrong z still in • Further improvements: • Improve mock samples, including more realistic photo-z errors (on new Millennium simulation) • Next science steps: • Most obvious: measure clustering of morphological classes (with Capak et al.)
Compute r0 and g for increasing photo-z errors Zph=Zsp+G(sz) Where G(sz) is a random deviate extracted from a Gaussian with dispersion sz= s0 (1+zsp) Crude first approximation Comparison with VVDS indicates then that average error should be s0~0.01 In reality, errors have more complex behaviour and systematic effect play major role (ongoing refined analysis)