Assessing Line-of-sight Projections in Cluster Finding

Anbo Chen, Gus EvrardUniversity of Michigan

2009 March @ SLAC

Collaborations in Progress

• Optical– Jiangang Hao (Michigan)

• SZ– Brian Nord (Michigan)

• Velocity Dispersion– Matt Becker (Chicago)

Outline

• The Halo Model• Model Parameters & Inputs• Predictions on optical projection in *BCG,

*=Max, GM, Ben, etc.• Predictions on SZ projections• Monte Carlo realizations• Mock Catalog capability• Velocity Dispersion

Building the Analytic Model

• Initial power spectrum (Eisenstein & Hu)

• Halo-halo correlation (Pillepich et al.)

• Projected Halos along a line-of-sight:

The Analytic Model continued

• HOD (Brown et al.)– N(Mass,z,MB)~(Mass-Mmin(MB,z))/Mscale(MB,z)

• Color Model (Hao et al.)– G-R mean and sigma for Red and Blue galaxies– Red/Blue fraction in central and satellite galaxies

– (Hao et~al.)

Verification with N-body Simulation

Target:

Mass 2x10^14

Objects:

+/- 0.025 in z

within r200

Implications:

1. Consistency

2. Correlation

3. Redshift-Dependency

Mean Projection Effect

Targeting on a dark matter halo (cluster) and calculate the

expected projection of galaxies

Projection in Optical (MaxBCG)

• Contamination Components– Left : precise measurement of r200

– Right: overestimated r200 (by 20%)

Red/Blue Galaxy Fraction

• Left Panel: Red Fraction = 80%

• Right Panel: Red Fraction = 90%

• Max_BCG is better in excluding red galaxies because of the color selection

Projection in SZ flux (B.Nord)• SZ flux contam

ination:• Color=redshift• Prediction corr

ect @ z=0.25• Black line <->

darker points

Monte Carlo Simulation

• Method– Calculate the probability of finding a halo

within each volume in space and mass

– Calculate the probability of having a galaxy in each volume in Color-Magnitude space according to HOD

Applications

• Provide a probability distribution, P(Ngalobs|Ngal

int)– Help understand the asymmetry in projection and the

bias introduced henceforth

• Create mock skies– ~20 sq.deg. considering halo-halo correlation– FAST (<1min), UNLIMITED– Can input different cosmologies!

• Simulate observations in galaxy velocity dispersion– Help understand the origin of the non-Gaussian backg

round

GMBCG run on the mock (J.Hao)

Understanding the Galaxy Velocity Dispersion (M.Becker)

• Driving factors:– Projection due

to correlation– Overestimation

in r200

Conclusion• Semi-analytic

• Multi-band photometry HOD

• Constrains on cosmology, cluster physics

• Expected mean projections– in optical cluster finding– In SZ cluster finding

• Monte Carlo applications– Mocks– Velocity dispersion

Thank you!

for being awake the whole time.