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Tracing Dark and Luminous Matter in COSMOS: Key Astrophysics and Practical Restrictions

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

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Tracing Dark and Luminous Matter in COSMOS: Key Astrophysics and Practical Restrictions

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

  2. Tracing the Matter Distribution with Lensing: Key Astrophysics 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) James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

  3. Tracing the Matter Distribution with Lensing: Key Astrophysics 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) James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

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

  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(Dls/Dos) Given @20 galaxies/’2 Need a 10’ aperture to overcome intrinsic noise Sensitivity drops at high z  Dls/Dos 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 N(>S) Z = 0.2 - 1.0 dz = 0.05 slices  (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? 5 2 2 1 3 4 3 1 6 7 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 5 4 3 James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

  10. How to Compare Mass Maps and Light Maps? 5 2 2 1 3 4 3 1 6 7 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 5 4 3 James Taylor (Caltech) -- Cosmos meeting -- Kyoto, Japan -- May 24 -- 2005

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

  12. 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 T log(M) Convergence Bin Convergence Bin Z = 0.2 - 0.4 Convergence Bin

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

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