1. Tracing Dark and Luminous Matter in COSMOS: Key Astrophysics and Practical Restrictions James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005 COSMOS high-z convergence map Richard Massey

2. Tracing the Matter Distribution with Lensing: Key Astrophysics James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005 1) Cosmological Parameters: Precision Measurements within the LCDM Paradigm * Power spectrum: total + evolution (shear power spectrum -> amplitude and evolution of fluctuations) * Features in the Power Spectrum (baryon wiggles) * Clustering, Higher Moments (power spectrum + Gaussianity) * Lens-Source Distance Ratios (equation of state/background cosmology) * Numbers of Individual Objects (cluster number counts, catalogs for follow-up) * Halo properties? Bread and butter science; cosmic variance a problem 2) Testing the LCDM, The Nature of Dark Matter, Fundamental Physics * Small-scale power (<- power spectrum features, CDM self-interaction, annihilation, decay, baryon coupling) * Halo Shapes (CDM vs. alternatives, constraints on modified gravity) * Halo Central Densities (CDM physics, power spectrum) * Halo Substructure (same + CDM-baryon interaction) Fundamentally important but plain vanilla model has survived similar tests so far 3) Mass versus Light (Baryons versus CDM) * Halo Occupation (galaxy formation; group and cluster luminosity functions) * Galaxy Environment (only statistical, but extend observed low-z trends to high z ) * (Individual) Galaxy Properties vs. Halo Properties (from gg lensing; fundamental issue in galaxy formation) * Halo Central Densities (adiabatic contraction, feedback, galaxy formation physics)

3. Tracing the Matter Distribution with Lensing: Key Astrophysics James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005 1) Cosmological Parameters: Precision Measurements within the LCDM Paradigm * Power spectrum: total + evolution (shear power spectrum -> amplitude and evolution of fluctuations) * Features in the Power Spectrum (baryon wiggles) * Clustering, Higher Moments (power spectrum + Gaussianity) * Lens-Source Distance Ratios (equation of state/background cosmology) * Numbers of Individual Objects (cluster number counts, catalogs for follow-up) * Halo properties? Bread and butter science; cosmic variance a problem 2) Testing the LCDM, The Nature of Dark Matter, Fundamental Physics * Small-scale power (<- power spectrum features, CDM self-interaction, annihilation, decay, baryon coupling) * Halo Shapes (CDM vs. alternatives, constraints on modified gravity) * Halo Central Densities (CDM physics, power spectrum) * Halo Substructure (same + CDM-baryon interaction) Fundamentally important but plain vanilla model has survived similar tests so far 3) Mass versus Light (Baryons versus CDM) * Halo Occupation (galaxy formation; group and cluster luminosity functions) * Galaxy Environment (only statistical, but extend observed low-z trends to high z ) * (Individual) Galaxy Properties vs. Halo Properties (from gg lensing; fundamental issue in galaxy formation) * Halo Central Densities (adiabatic contraction, feedback, galaxy formation physics)

4. Expectations: Number of virialized halos N(>M) Halo Mass (M o ) Mass function integrated from z = 0 - 1.5, over 2 s.d. (cosmic variance from sigma(Vol) + shot noise)  possible weak lensing detections  current detections

5. Expectations: Number of Lensing Detections Treat halos as isothermal spheres of velocity dispersion  Shear signal goes as: S = 2.4% (15’/  ap ) (  /1000 km/s) 2 (D ls /D os ) Given @20 galaxies/’ 2 Need a 10’ aperture to overcome intrinsic noise Sensitivity drops at high z  D ls /D os Overall, expect, approx. 1 source at S/N @ 3-4 10 @ 2-3, 100 @ 1-2 in current maps So only a few objects are obvious detections, but there is signal in the low-sigma peaks Z = 0.2 - 1.0 dz = 0.05 slices N(>S)  (km/s)

6. Expectations: Completeness and Contamination Hennawi & Spergel (2004): n vs efficiency e e = real/(real+contamination)

7. Expectations: Completeness and Contamination Hennawi & Spergel (2004): n vs efficiency e e = real/(real+contamination)

8. How to Compare Mass Maps and Light Maps? Problem: low signal-to-noise in both maps on the scales of interest (5-10’), complicated systematics / projection effects Two strategies: - consider theoretical expectations - search adaptively around peaks James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

9. How to Compare Mass Maps and Light Maps? Problem: low signal-to-noise in both maps on the scales of interest (5-10’), complicated systematics / projection effects Two strategies: - consider theoretical expectations - search adaptively around peaks 1 2 3 6 5 7 1 3 4 2 2 5 4 3 1 James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

10. How to Compare Mass Maps and Light Maps? Problem: low signal-to-noise in both maps on the scales of interest (5-10’), complicated systematics / projection effects Two strategies: - consider theoretical expectations - search adaptively around peaks 1 2 3 6 5 7 1 3 4 2 2 5 4 3 1 James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

11. Shear vs. photo-z around peaks, along promising lines of sight

13. Summary * Lots of well-defined science to do with weak lensing: testing the concordance model, constraining parameters, relating mass and light * Cosmic variance a problem for statistical tests; intrinsic difficulties (low S/N, projection effects) a problem for object-based tests * Need to use luminous data to maximum effect (role as test-bed for future surveys) * Example: even without sophisticated filtering, detect approx. 5-10 good cluster candidates along various lines of sight * Can also do semi-statistical studies? (binning over med. S/N data) James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

14. Example: Galaxy Properties by Projected Environment Bin galaxies by local convergence in the medium-redshift map  7 bins should sort by mean density, especially between z = 0.2 and 0.9

15. Example: Galaxy Properties by Projected Environment Z = 0.2 - 0.9 Z = 0.2 - 0.4 Convergence Bin T log(M)

16. Example: Galaxy Properties by Projected Environment Z = 0.2 - 0.9 Z = 0.2 - 0.4 Convergence Bin T log(M)