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SZ effects by using high-resolution simulations Lauro Moscardini Dipartimento di Astronomia Università di Bologna, Italy lauro.moscardini@unibo.it Orsay, Paris, 7-8th april 2005 Works in collaboration with:S. Borgani,A. Diaferio, K. Dolag,G. Murante,M. Roncarelli,V. Springel, G. Tormen, L. Tornatore, P. Tozzi. Works in collaboration with: S. Borgani, A. Diaferio, K. Dolag, G. Murante, M. Roncarelli, V. Springel, G. Tormen, L. Tornatore, P. Tozzi. Mainly based on Diaferio et al. 2005, MNRAS, 356, 1477; Roncarelli et al. 2005, in preparation; Bonaldi et al. 2005, in preparation

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A Tree+SPH high-res. Simulation of the cosmic web KP Collaboration: S. Borgani, A. Diaferio, K. Dolag, L. Moscardini, G. Murante, V. Springel, G. Tormen, L.Tornatore, P.Tozz i L = 192 h -1 Mpc ; N gas =N DM = 480 3 Pl = 7.5 h -1 kpc ; m gas = 6.9 10 8 h -1 M ⊙ 40,000 CPU hours and 100 Gb RAM, using 64 processors of IBM- SP4 in CINECA (INAF grant); about 1.2 Tb of data produced. Code: Tree + SPH GADGET (Springel et al. 2001, 2002) www.MPA-Garching.MPG.DE/gadget Radiative cool.+UV backgr. Multiphase model for star-formation and model galactic winds. CDM cosmology: m = 1- 0.3, bar 0.02h -2, h=0.7, 8 =0.8 Co-workers: M. Arnaboldi, L.M. Cheng, S. Ettori, O. Gerhard, E. Rasia, M. Roncarelli, plus other students

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400 clusters with > 10 4 particles. X-ray cluster scaling properties and nature of their scatter. Contribution of diffuse gas to the soft X-ray background. SZ effect from clumped and diffuse gas. Comparing cluster masses: X-ray, lensing, optical and SZ. Diffuse intracluster light on a statistical basis. oom-in simulations of clusters and other interesting regions. Populate the box with simulated & SAM galaxies

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Systematics in the measurements of cluster peculiar velocities Question: How well can we measure the peculiar velocity of clusters combining the Sunyaev-Zel’dovich effects? See Diaferio et al. 2005, MNRAS, 356, 1477

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The cluster sample 117 clusters @ z=0, with M vir >10 14 M sun Pixel size 42 kpc/h

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No Systematics from velocity (i)Is the gas bulk velocity equivalent to the DM bulk velocity? (ii)What is the average effect of the internal bulk velocity? Mean absolute deviation: 18 km/s Uncertainty smaller than 200 km/s at 93% level

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Electronic vs. X-ray temperature T e = a + b T x Are X-ray temperature equivalent to electronic number density? Answer: Only in the cluster internal parts (r lim <0.1 R vir ). In spatially poorly resolved clusters, using T X rather T e can substantially overestimate the peculiar velocity.

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Estimated vs. actual velocities resolvedclusters unresolvedclusters X-ray Temp. electronic Temp.

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Scaling relations: (i) central Compton param. vs. X-ray Luminosity self-similar expectation: y 0 L x 3/4 E 1/4 (z) Simulated clusters: slope (0.79 0.02) Real clusters: Open: Mc Carthy et al. (2003) slope (0.65 0.04) Solid: Cooray (1999) slope (0.47 0.07) Discrepancy between datasets!

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Scaling relations: (ii) central Compton param. vs. X-ray temperature self-similar expectation: y 0 T x 3/2 E(z) Simulated clusters: slope (1.55 0.03) Real clusters: Mc Carthy et al. (2003) slope (2.24 0.39) Cooray (1999) slope (1.87 0.31) Benson et al. (2004) slope (2.79 0.51)

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Scaling relations: (iii) SZ flux decrement vs. X-ray e.w. temperature self-similar expectation: S d A 2 E(z) T 5/2 Simulated clusters: slope (2.41 0.11) Real clusters: Benson et al. (2004) slope (2.26 0.38)

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How the cluster SZ properties depend on the physical processes included in the simulations? in collaboration with A. Bonaldi, Padova K. Dolag, Garching E. Rasia, Padova G. Tormen, Padova

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The hydro-simulations The sample of 11 simulated clusters has been extracted from Hutt (High resolUtion clusTer seT, Dolag et al. 2005) The sample of 11 simulated clusters has been extracted from Hutt (High resolUtion clusTer seT, Dolag et al. 2005) Mass resolution for gas particles: 2 x 10 8 solar masses Mass resolution for gas particles: 2 x 10 8 solar masses Masses at z=0 are between 2 x 10 14 and 2 x 10 15 solar masses Masses at z=0 are between 2 x 10 14 and 2 x 10 15 solar masses (Mass-weighted) temperatures are between 1 and 10 keV (Mass-weighted) temperatures are between 1 and 10 keV

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4 different sets of physical processes included in the simulations Gas: Gas: only adiabatic gas Gas_nv: Gas_nv: low-viscosity scheme Csf: Csf: cooling, star formation and SN feedback Csfc: Csfc: like csf plus thermal conduction

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SZ profiles Solid: gas Dashed: gas_nv Dashed-dotted: csf Dotted: csfc Physical processes are changing the SZ profiles in the central regions, mainly in small objects

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Scaling relations: y 0 vs. T M Expected slope from self-similar model is 1.5: from the simulations we obtain values between 1.3 for gas_nv and 1.8 for csfc. No effects for the y 0 -L x and S-T relations, where we recover the expected slopes 0.75 and 2.5 independently of the physics

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SZ Simulator Convolve IMAGE with instrumental DIRTY BEAM Convert y-parameter into measured flux [mJy] Add gaussian thermal noise at the appropriate level (exposition time depending) Smooth image to reduce noise Run CLEAN deconvolution algorithm CLUSTER y map Instrument DIRTY BEAM Anna Bonaldi OBSERVED CLUSTER Observation report file Simulated cluster Observed cluster AMI “survey mode” Kneissl et al. (2001) FWHM=4.8 arcmin

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The “observed” SZ profiles

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First conclusions Gas-DM velocity bias is negligible Gas-DM velocity bias is negligible Internal bulk flows introduce 200 km/s uncertainty (Holder 2003; Nagai et al. 2003) Internal bulk flows introduce 200 km/s uncertainty (Holder 2003; Nagai et al. 2003) Using T X rather than T e can introduce a serious overestimate of the peculiar velocity Using T X rather than T e can introduce a serious overestimate of the peculiar velocity The simulated scaling relations agree with self- similar predictions and (roughly) with observations The simulated scaling relations agree with self- similar predictions and (roughly) with observations But possible dependences on physical processes and instrumental properties But possible dependences on physical processes and instrumental properties

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The contribution from the cosmic web in collaboration with M. Roncarelli, Bologna S. Borgani, Trieste K. Dolag, Garching plus the KP collaboration

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MAPMAKING (1.9 deg) 2 (3.8 deg) 2 1.Choice of the right output 2.Randomization 3.Overlapping Roncarelli et al. in preparation Computational problem: to recover the past-light cone up to z=6 we need to use 90 outputs, i.e. 1 Terabyte of data! 10 different maps available now

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Thermal Sunyaev-Zel’dovich effect y - parameter

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Redshift contribution to the y-parameter Mean y - parameter: Total: 1.19 x 10 -6 WHIM: 6.90 x 10 -7

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Kinetic Sunyaev-Zel’dovich effect b - parameter

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Angular correlation function for thermal SZ

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Future steps Complete statistical analysis of an extended set of maps Complete statistical analysis of an extended set of maps Clustering analysis of SZ and X-ray maps plus cross-correlation Clustering analysis of SZ and X-ray maps plus cross-correlation Detectability of high-redshift clusters with ALMA (and Planck) via realistic simulations of the observations Detectability of high-redshift clusters with ALMA (and Planck) via realistic simulations of the observations Redshift evolution of the scaling relations Redshift evolution of the scaling relations

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