1 / 17

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

marly
Download Presentation

Assessing Line-of-sight Projections in Cluster Finding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related