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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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Presentation on theme: "Evolution of clustering in COSMOS: a progress report Luigi Guzzo (INAF, Milano) N. Scoville, O. Le Fevre, A. Pollo, B. Meneux, & COSMOS Team."— Presentation transcript:

1 Evolution of clustering in COSMOS: a progress report Luigi Guzzo (INAF, Milano) N. Scoville, O. Le Fevre, A. Pollo, B. Meneux, & COSMOS Team

2 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 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 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 Final goal: measure evolution of how clustering depends on galaxy properties

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4 Redshift-space correlations simply destroyed by photo-z errors 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 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):

5 Destructive effect of the large photo-z errors on redshift-space correlation function:

6 Recover diluted signal by projecting (r p, ) along the line of sight and fitting a power-law real-space correlation function (r)=(r/r 0 ) - Recover diluted signal by projecting (r p, ) along the line of sight and fitting a power-law real-space correlation function (r)=(r/r 0 ) -

7 I AB < < RA < & 1.5 < DEC < 2.9 Use very thick redshift slices (>> photo-z error): 0.2 – galaxies 0.4 – galaxies 0.6 – galaxies 0.8 – galaxies and project power back onto w p (r p ) Compute and project power back onto w p (r p ) Fit power-law (r) and recover amplitude and slope at each redshift Fit power-law (r) and recover amplitude and slope at each redshift Application to May 2005 COSMOS photo-z catalogue:

8 Montecarlo realization of catalogue mask

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10 Results (blue) and comparison to VVDS (red)

11 Old results from Nov 2004 catalogue version

12 Encouraging result: the catalogue has improved! The recovered evolution of the correlation length r 0 and slope of (r) is fully consistent with what is measured in the VVDS spectroscopic survey (Le Fevre et al. astro-ph/ ) 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.) Remarks and perspectives:

13 Test with mock COSMOS: degrade zs as for photo-z

14 Compute r 0 and for increasing photo-z errors Z ph =Z sp +G( z ) Where G( z ) is a random deviate extracted from a Gaussian with dispersion z = 0 (1+z sp ) z = 0 (1+z sp ) Crude first approximation Crude first approximation Comparison with VVDS indicates then that average error should be 0 ~0.01 Comparison with VVDS indicates then that average error should be 0 ~0.01 In reality, errors have more complex behaviour and systematic effect play major role (ongoing refined analysis) In reality, errors have more complex behaviour and systematic effect play major role (ongoing refined analysis)

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