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Observational Constraints on Galaxy Clusters and DM Dynamics. Doron Lemze Tel-Aviv University / Johns Hopkins University. Collaborators :. Tom Broadhurst, Yoel Rephaeli, Rennan Barkana, Keiichi Umetsu, Rick Wagner, & Mike Norman. 21/9/10. Overview :.

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Observational Constraints on Galaxy Clusters and DM Dynamics

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Observational constraints on galaxy clusters and dm dynamics

Observational Constraints on Galaxy

Clusters and DM Dynamics

Doron Lemze

Tel-Aviv University / Johns Hopkins University

Collaborators:

Tom Broadhurst, Yoel Rephaeli, Rennan Barkana, Keiichi Umetsu,

Rick Wagner, & Mike Norman

21/9/10


Observational constraints on galaxy clusters and dm dynamics

Overview:

  • Observational constraints on galaxy clusters.

  • Study case: the high-mass cluster A1689

Lemze, Broadhurst, Rephaeli , Barkana, & Umetsu 2009

  • DM Dynamics

Lemze, Rephaeli , Barkana, Broadhurst, Wagner, & Norman 2010


Observational constraints on galaxy clusters and dm dynamics

The measuring instruments

VLT/VIMOS

Hubble

Subaru/suprime-cam

Cluster galaxies

spectroscopy

Strong lensing

Weak lensing

Imaging of cluster galaxies


Observational constraints on galaxy clusters and dm dynamics

cD galaxy

Lemze, Barkana, Broadhurst & Rephaeli 2008

0

0.5’


Observational constraints on galaxy clusters and dm dynamics

Galaxy surface number density

About 1900 cluster members.


Observational constraints on galaxy clusters and dm dynamics

Velocity-Space Diagram

Velocity caustics method:

Diaferio & Geller 1997

Diaferio 1999

About 500 cluster members.


Observational constraints on galaxy clusters and dm dynamics

Methodology

Galaxy surface number density

Projected velocity dispersion

Jeans eq.

Velocity anisotropy

The unknowns:

M is taken from lensing


Observational constraints on galaxy clusters and dm dynamics

The fit results

Galaxy surface

number density fit

Galaxy surface number density

data points : 20

Projected velocity dispersion

data points : 10

The number of free

parameters : 5

---------------------------------------

dof : 25

Projected velocity

dispersion fit


Observational constraints on galaxy clusters and dm dynamics

Galaxy number density profile


Observational constraints on galaxy clusters and dm dynamics

Velocity anisotropy profile


Observational constraints on galaxy clusters and dm dynamics

Galaxy velocity anisotropy data vs. simulations

Arieli, Rephaeli, & Norman 2010


Observational constraints on galaxy clusters and dm dynamics

Mass profiles

Here M is not taken from lensing!


Observational constraints on galaxy clusters and dm dynamics

The high concentration problem

Broadhurst et al. 2008

Zitrin et al. 2010


Observational constraints on galaxy clusters and dm dynamics

Can we trust the high value found?

A1689

MS2137

Comerford & Natarajan 2007


Observational constraints on galaxy clusters and dm dynamics

Virial mass vs. concentration parameter

Here M is not taken from lensing!


Observational constraints on galaxy clusters and dm dynamics

Building statistical samples

62 clusters

Hennawi et al. 2007

Bullock et al. 2007

Comerford & Natarajan 2007


Observational constraints on galaxy clusters and dm dynamics

Building statistical samples

10 halos per data bin

X-ray measurements

N-body simulations using WMAP5 parameters

Duffy et al. 2007


Observational constraints on galaxy clusters and dm dynamics

Large samples

Weak lensing measurements of stacked

SDSS groups and galaxy clusters

Johnston et al. 2007

In agreement with Mandelbaum et al.

2006


Observational constraints on galaxy clusters and dm dynamics

Conclusions

  • We constrained the virial mass using galaxy positions and velocities data.

  • We deduced high values for the concentration parameter using two independent methods.

  • We estimated for the first time a detailed 3D velocity profile.

  • We found that the caustic mass is a good estimation for the mass profile.

  • Our three independent estimates for the mass profile are consistent with each other.


Observational constraints on galaxy clusters and dm dynamics

DM dynamics

Question: how one can determine DM dynamics when

“DM spectroscopy” is hard to obtain?

Answer: by using a surrogate Measurement. The first

Choice should be other kind of collisionless particles - galaxies.

The orbit of a test particle in a collisionless gravitational system is

independent of the particle mass. This would presumably imply

that once hydrostatic equilibrium is attained, most likely as a result

mixing and mean field relaxation, DM and galaxies should have the

same mean specific kinetic energy, i.e., ,

where


Observational constraints on galaxy clusters and dm dynamics

DM velocity anisotropy

Host et al. 2009

Best-fit value:


Observational constraints on galaxy clusters and dm dynamics

DM density

DM density

Galaxy density

All other colors

Total matter density

Best-fit values:


Observational constraints on galaxy clusters and dm dynamics

The collisionless profile

Model-dependent

Model-independent


Observational constraints on galaxy clusters and dm dynamics

The velocity bias profile

Model-independent

Model-dependent


Observational constraints on galaxy clusters and dm dynamics

Conclusions

  • We obtain the mean value of the DM velocity anisotropy parameter, and the DM density profile.

  • r ∼ 1/3 r_vir seems to be a transition region interior to which collisional effects significantly modify the dynamical properties of the galaxy population with respect to those of DM in A1689


Observational constraints on galaxy clusters and dm dynamics

The End


Observational constraints on galaxy clusters and dm dynamics

Building statistical samples

62 clusters

Hennawi et al. 2007

Bullock et al. 2007

??C is measured using lensing and X-ray???

Comerford & Natarajan 2007


Observational constraints on galaxy clusters and dm dynamics

10 halos per data bin

X-ray measurements

N-body simulation using WMAP5 parameters – lower sigma_8

Duffy et al. 2007


Observational constraints on galaxy clusters and dm dynamics

What has been done previously?

For obtaining the mass profile:

Assuming

a gas density profile

Assuming

a temperature profile

Fitting a double

Surface brightness

model

X-ray data

model

Double

Isothermal

Where a single model is

For gas in hydrostatic equilibrium

,

, and

.

For the isothermal assumption:

where

Assuming

a DM profile

Lensing data

NFW


Observational constraints on galaxy clusters and dm dynamics

Combining Lensing, X-ray, and galaxy dynamic

Measurements in Clusters

Doron Lemze

Tel-Aviv University

Collaborators:

Collaborators:

Tom Broadhurst , Rennan Barkana, Yoel Rephaeli, Keiich Umetsu


Observational constraints on galaxy clusters and dm dynamics

Large samples

Weak lensing measurements of stacked

SDSS groups and galaxy clusters

Johnston et al. 2007

Black points are from the shear profile

fits for the L200 luminosity bins and the

red points are from the N200 richness bins.

In agreement with Mandelbaum et al.

2006


Observational constraints on galaxy clusters and dm dynamics

In

Rachel Mandelbaum, Uros Seljak, Christopher M. Hirata 2008

Astro-ph 0805.2552v2

FIG. 5: The best-fit c(M) relation at z = 0.22 with the 1

allowed region indicated. The red points with errorbars show

the best-fit masses and concentrations for each bin when we

fit them individually, without requiring a power-law c(M) relation.

The blue dotted lines show the predictions of [39] for

our mass definition and redshift, for theWMAP1 (higher) and

WMAP3 (lower) cosmologies. The prediction for theWMAP5

cosmology falls in between the two and is not shown here.

Their measurements are actually lower than the theoretical model

Eventhough they have used WMAP1 (which gives a lower curve see

Duffy et al. 2007). This indicate that they stack the clusters without

Exactly center them ontop of each other and didn’t separate the background

From the cluster galaxy good. These two effect lowers the concentration value.


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