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Assessing Line-of-sight Projections in Cluster Finding. Anbo Chen, Gus Evrard University 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 - PowerPoint PPT Presentation
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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.