porciano kyoto

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Information about porciano kyoto

Published on August 29, 2007

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Angular clustering and halo occupation propertiesof COSMOS galaxies:  Angular clustering and halo occupation properties of COSMOS galaxies Cristiano Porciani Overview:  Overview Brief summary of clustering studies performed at ETH-Zurich: correlation functions counts in cells theoretical interpretation: halo occupation models A few suggestions for optimizing the photometric redshift catalog for LSS studies (many thanks to P. Capak and B. Mobasher for producing it!) LSS from COSMOS :  LSS from COSMOS Finite volume effects: integral constraint, lack of fluctuations on large-scales. Spatially-dependent photometry Poor sampling, (lack of close galaxy pairs) mask accuracy, galaxy size and identification in crowded regions The COSMOS galaxy catalog is ideal for surveying the LSS of the universe beyond the local volumes probed by the 2dFGRS and the SDSS COSMOS data (LSE) Power-law fit Coil et al. (2004) - 5 deg2 over 5 fields Wilson (2003) - 1.5 deg2 over 6 fields Our preliminary mask is probably too little conservative:  Our preliminary mask is probably too little conservative Our preliminary mask sampled with a Poisson point process containing 106 points [my 'random catalog' for computing ()]. COSMOS galaxies with Iandlt;25 COSMOS galaxies with Iandlt;25 and no photometric redshift Sample variance and the IC:  Sample variance and the IC The shape of the PDF, the variance and the bias of an estimator for () depends on: the adopted estimator the angular separation (nearly Gaussian PDF for small , strongly skewed PDF for large ) the higher-order moments of the galaxy distribution shot-noise finite volume effects IC Poisson errors strongly underestimate the variance and do not give the covariance matrix; groupwise bootstrap (e.g. Porciani andamp; Giavalisco 2002) or mock catalogs (need many!!!) are better alternatives. Slide6:  Clustering amplitude vs apparent magnitude Points with errorbars represent COSMOS data. The dashed lines are the best-fitting power-laws from Coil et al. (2004). Slide7:  Clustering amplitude vs apparent magnitude COSMOS Coil et al. (2004) Postman et al. (1998) Millennium mock COSMOS created by Kitzbichler et al. Slide8:  Clustering amplitude vs color All COSMOS galaxies with Iandlt;24 Slide9:  Clustering amplitude vs color COSMOS Coil et al. (2004) Millennium mock COSMOS by Kitzbichler et al. All COSMOS galaxies with Iandlt;24 Slide10:  All COSMOS galaxies with Iandlt;25 Clustering amplitude vs color Slide11:  Adding the redshift information Joint distribution of z and z for our photometric redshifts A non-negligible fraction of our galaxies have very uncertain redshifts Slide12:  COSMOS galaxies with Iandlt;24 and 0.2andlt;zandlt;0.5 COSMOS galaxies with Iandlt;24, 0.2andlt; zandlt;0.5 and z andlt; 0.2 (1+z) The clustering amplitude differs by 25%! Clustering amplitude and redshift errors Clustering amplitude and redshift errors:  Clustering amplitude and redshift errors It would be crucial to have information regarding the likelihood function of the photometric redshifts. This way each galaxy could be associated with a statistical weight to measure (). If it is not possible to have the likelihood function of each COSMOS galaxy, a good proxy (e.g. a substantial number of percentiles) would be very helpful as well. This would also increase the accuracy of the Limber inversion. Likelihood functions are essential to accurately estimate () Slide14:  Redshift evolution IABandlt; 24 The halo model for large-scale structure:  The halo model for large-scale structure Slide16:  The halo model provides a phenomenological understanding of LSS in the non-linear clustering regime. Motivations Galaxies are expected to form within DM halos but a detailed understanding of the physics is still lacking The number density and the clustering properties of DM halos can be readily and reliably computed as a function of their mass It is therefore of great interest to try to establish a connection between these halos and different classes of cosmic objects This phenomenological description is useful to guide and constrain galaxy formation models and to build mock galaxy catalogues (in order to understand systematic effects in galaxy surveys) The halo model successfully reproduces low order mass and galaxy correlations (and pair velocities) in numerical simulations. Slide17:  The halo occupation distribution:  The halo occupation distribution The key ingredient of the halo model for galaxy clustering is the halo occupation distribution, P(N | M), which gives the probability for a halo of virial mass M to contain N galaxies of a given type. In principle, its moment of order n can be determined by studying the n-point clustering properties of the galaxy population. Relying on measurements of the mean density (need completeness corrections) and the 2-point correlation function of COSMOS galaxies, only the first 2 moments of the halo occupation function can be determined from the data. These strongly constrain galaxy formation models! 2-point statistics in the halo model:  2-point statistics in the halo model Two-point correlation function: Mean density of galaxies: Halo mass function Halo occupation number Galaxy number density profile Second factorial moment of the HOD Halo correlation funct. Previous experience with the halo model:  Previous experience with the halo model LBGs: Porciani andamp; Giavalisco (2002) 2dFGRS: Magliocchetti andamp; Porciani (2003) 2QZ: Porciani, Magliocchetti andamp; Norberg (2004) Porciani andamp; Norberg (2005) All the machinery is in place, ready to be applied to the final COSMOS data Slide21:  Potential to study redshift, luminosity, morphology and color dependent clustering in terms of halo occupation properties COSMOS 2dFGRS Further activities at ETH-Zurich :  Further activities at ETH-Zurich Counts-in-cells analysis higher-order moments Clustering of drop-out galaxies at high-z Preliminary theoretical studies to investigate the potentiality of ZCOSMOS Slide23: 

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