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Assessing Line-of-sight Projections in Cluster Finding

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

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Assessing Line-of-sight Projections in Cluster Finding

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  1. Assessing Line-of-sight Projections in Cluster Finding Anbo Chen, Gus Evrard University of Michigan 2009 March @ SLAC

  2. Collaborations in Progress • Optical • Jiangang Hao (Michigan) • SZ • Brian Nord (Michigan) • Velocity Dispersion • Matt Becker (Chicago)

  3. 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

  4. Building the Analytic Model • Initial power spectrum (Eisenstein & Hu) • Halo-halo correlation (Pillepich et al.) • Projected Halos along a line-of-sight:

  5. 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.)

  6. Verification with N-body Simulation Target: Mass 2x10^14 Objects: +/- 0.025 in z within r200 Implications: 1. Consistency 2. Correlation 3. Redshift-Dependency

  7. Mean Projection Effect Targeting on a dark matter halo (cluster) and calculate the expected projection of galaxies

  8. Projection in Optical (MaxBCG) • Contamination Components • Left : precise measurement of r200 • Right: overestimated r200 (by 20%)

  9. 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

  10. Projection in SZ flux (B.Nord) • SZ flux contamination: • Color=redshift • Prediction correct @ z=0.25 • Black line <-> darker points

  11. 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

  12. Applications • Provide a probability distribution, P(Ngalobs|Ngalint) • 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 background

  13. GMBCG run on the mock (J.Hao)

  14. Understanding the Galaxy Velocity Dispersion (M.Becker) • Driving factors: • Projection due to correlation • Overestimation in r200

  15. 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

  16. Thank you! for being awake the whole time.

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