Shear Power of Weak Lensing. Wayne Hu U. Chicago

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1 Shear Power of Weak Lensing 10 3 N-body Shear 300 Sampling errors l(l+1)c l /2π εε Error estimate Shot Noise θ y (arcmin) l θ x (arcmin) Wayne Hu U. Chicago

2 Collaborators Asantha Cooray Dragan Huterer Mike Joffre Jordi Miralda-Escude Max Tegmark Martin White

3 Collaborators Asantha Cooray Dragan Huterer Mike Joffre Jordi Miralda-Escude Max Tegmark Martin White Microsoft

4 Why Power Spectrum? Pros: Direct observable: shear-based Statistical properties simple on large-scales (cf. variance, higher order) Complete statistical information on large-scales

5 Why Power Spectrum? Pros: Direct observable: shear-based Statistical properties simple on large-scales (cf. variance, higher order) Complete statistical information on large-scales Statistical tools developed for CMB and LSS power spectrum analysis directly applicable Information easily combined with CMB As (more with redshifts) precise as CMB per area of sky

6 Pros: Why Power Spectrum? Direct observable: shear-based Statistical properties simple on large-scales (cf. variance, higher order) Complete statistical information on large-scales Statistical tools developed for CMB and LSS power spectrum analysis directly applicable Information easily combined with CMB As (more with redshifts) precise as CMB per area of sky Cons: Relatively featureless Non-linear below degree scale Degeneracies: initial spectrum, transfer function shape, density growth, and angular diameter distance + source redshift distribution Computationally expensive: extraction by likelihood analysis N pix 3

7 Statistics and Modelling

8 Shear Power Modes Alignment of shear and wavevector defines modes ε β

9 Power Spectrum Roadmap Lensing weighted Limber projection of density power spectrum ε-shear power = κ-power εε l(l+1)cl /2π k=0.02 h/mpc zs Linear Non-linear 10 7 Limber approximation l zs=1 Kaiser (1992) Jain & Seljak (1997) Hu (2000)

10 Statistics & Simulations Measurement of power in each multipole is independent if the field is Gaussian Non-linearities make the power spectrum non-gaussian Need many simulations to test statistical properties of the field in particular: sample variance White & Hu (1999)

11 Statistics & Simulations Measurement of power in each multipole is independent if the field is Gaussian Non-linearities make the power spectrum non-gaussian Need many simulations to test statistical properties of the field in particular: sample variance PM simulations ideal if sufficient angular resolution can be achieved lenses sources obs. tiling White & Hu (1999)

12 PM Simulations Hundreds of independent simulations Convergence Shear 6 6 FOV; 2' Res.; h 1 Mpc box; h 1 kpc mesh; M White & Hu (1999)

13 Mean & Sampling Errors Mean agrees well with PD96 + Limber (Jain, Seljak & White 1999) Sampling errors per 6 x 6 field: Ratio Power PD96 linear shot noise vs PD96 White & Hu (1999)

14 Mean & Sampling Errors Sampling errors ~Gaussian at l<1000 Non-Gaussianity increases sampling errors on binned power spectrum At current survey depths, shot noise dominates in non-gaussian regime Ratio Sampling Errors vs shot noise 100 l Gaussian White & Hu (1999)

15 Mean & Sampling Errors Correlation in Band Powers: Correlation 1000 Gaussian 100 l l White & Hu (1999)

16 Mean & Sampling Errors Correlation in Band Powers: Non-Gaussian Correlation 1000 Gaussian 100 l l White & Hu (1999)

17 Halo Model Model density field as (linearly) clustered NFW halos of PS abundance: Simulation Shear 6 6 FOV; 2' Res.; h 1 Mpc box; h 1 kpc mesh; M White & Hu (1999)

18 Halo Model Model density field as (linearly) clustered NFW halos of PS abundance: Halo Model Shear Peebles (1974); Scherrer & Bertschinger (1991) Komatsu & Kitiyama (1999); Seljak (2001)

19 Power Spectrum Statistics in Halo Model Power spectrum as a function of largest halo mass included Non-linear regime dominated by halo profile / individual halos increased power spectrum variance and covariance 10 4 εε l(l+1)cl /2π log(m/m )< profile l Cooray, Hu, Miralda-Escude (2000)

20 Halo Model vs. Simulations Halo model for the trispectrum: power spectrum correlation Simulation Correlation 1000 Halo 100 l l Cooray & Hu (2001)

21 Halo Model vs. Simulations Halo model for the bispectrum: S 3 dominated by massive halos 10 3 Hu & White (1999) S σ (arcmin) Cooray & Hu (2001)

22 Halo Model vs. Simulations Explains large sampling errors: S σ (arcmin) White & Hu (1999)

23 Power Spectrum Estimation

24 Likelihood Analysis Pros: Optimal power spectrum estimator for Gaussian field Automatically accounts for irregular (sparse sampled) geometries & varying sampling densities Arbitrary noise correlations Marginalize over systematic error templates (ε, β, cross) checks for non-gravitational effects Rigorous error analysis in linear regime

25 Pros: Likelihood Analysis Optimal power spectrum estimator for Gaussian field Automatically accounts for irregular (sparse sampled) geometries & varying sampling densities Arbitrary noise correlations Marginalize over systematic error templates (ε, β, cross) checks for non-gravitational effects Rigorous error analysis in linear regime Cons: Computationally expensive search in multiple dimensions Not guaranteed to be optimal or unbiased in non-linear regime

26 Pros: Likelihood Analysis Optimal power spectrum estimator for Gaussian field Automatically accounts for irregular (sparse sampled) geometries & varying sampling densities Arbitrary noise correlations Marginalize over systematic error templates (ε, β, cross) checks for non-gravitational effects Rigorous error analysis in linear regime Cons: Computationally expensive search in multiple dimensions Not guaranteed to be optimal or unbiased in non-linear regime Test iterated quadratic estimator of maximum likelihood solution and error matrix

27 Testing the Likelihood Method Input: pixelized shear data γ 1 (n i ), γ 2 (n i ); pixel-pixel noise correlation Iterate in band power parameter space to maximum likelihood... Output: maximum likelihood band powers and local curvature for error estimate including covariance l(l+1)c l /2π εε N-body Shear Sampling errors Error estimate Shot Noise θ y (arcmin) Hu & White (2001) l θ x (arcmin)

28 Testing the Likelihood Method Sparse sampling test Mean and errors correctly recovered 10 3 N-body Shear 300 Sampling errors l(l+1)c l /2π εε Error estimate Shot Noise θ y (arcmin) Hu & White (2001) l θ x (arcmin)

29 Testing the Likelihood Method Systematic error monitoring: example underestimated shot noise β-channel appearance in power Systematic error template: jointly estimate or marginalize 10 3 Noise Underestimated Signal+Noise Noise Removed l(l+1)c l /2π 10 4 εε εε 10 5 ββ p noise Shot Noise Shot Noise l Hu & White (2001) l

30 Cosmological Parameter Forecasts

31 Degeneracies All parameters of ICs, transfer function, growth, angular diam. distance Power spectrum lacks strong features: degeneracies εε l(l+1)cl /2π k=0.02 h/mpc zs Linear Non-linear 10 7 Limber approximation l zs=1

32 Degeneracies All parameters of ICs, transfer function, growth, angular diam. distance Power spectrum lacks strong features: degeneracies T MAP Planck CMB: provides high redshift side IC's, transfer fn., ang. diameter to z=1000 Lensing: dark energy, dark matter Hu & Tegmark (1999) 20 CMB l (multipole) Redshifts: source (and lens) Breaks degeneracies by tomography Hu (1999)

33 Tomography Divide sample by photometric redshifts Cross correlate samples g i (D) ni(d) Order of magnitude increase in precision even after CMB breaks degeneracies Hu (1999) (a) Galaxy Distribution (b) Lensing Efficiency D Power deg 2, z med = l

34 Error Improvement Error Improvement: 25deg 2 MAP + 3-z no-z 1 MAP 9.6% 14% % 0.28 Ω Ω m h 2 b h 2 w τ n S T/S Ω Λ Cosmological Parameters δ ζ Hu (1999; 2001)

35 Error Improvement Error Improvement: 1000deg 2 MAP + 3-z no-z 1 MAP 9.6% 14% % 0.28 Ω Ω m h 2 b h 2 w τ n S T/S Ω Λ Cosmological Parameters δ ζ Hu (1999; 2001)

36 Dark Energy & Tomography Both CMB and tomography help lensing provide interesting constraints on dark energy 2 1 MAP no z 3 z w deg Ω Λ l<3000; 56 gal/deg2 Hu (2001)

37 Direct Detection of Dark Energy? In the presence of dark energy, shear is correlated with CMB temperature via ISW effect z>1.5 Θε l(l+1)cl /2π deg2 65% sky 1<z<1.5 z< l Hu (2001)

38 CMB Lensing: Tomography Anchor CMB acoustic waves are the ultimate high redshift source! Hu (2001)

39 Mass Reconstruction CMB acoustic waves are the ultimate high redshift source! original mass (deflection) map reconstructed 1.5' beam; 27µK-arcmin noise Hu (2001)

40 Power Spectra Power spectrum of deflection and cross correlation with CMB ISW 10 9 dd l(l+1)cl /2π 10 7 Θd l(l+1)cl /2π Planck Ideal l l Extend tomography to larger scales and higher redshift Constrain clustering properties of dark energy through ISW correlation Hu (2001)

41 Lens Redshifts Shear selected halos: clustered according to well-understood halo bias Shear Halo Identification Mo & White (1996) e.g. aperture mass: Schneider (1996)

42 Lens Redshifts Shifting of feature(s) in the angular power spectrum: d A (z) 10 0 hh l(l+1)cl /2π 10 1 z= z= l 4000deg 2, M>10 14 Cooray, Hu, Huterer, Joffre (2001)

43 Lens Redshifts If baryon features not detected, 10% distances to z~0.7 possible 10 0 hh l(l+1)cl /2π 10 1 z= z= l 4000deg 2, M>10 14 Cooray, Hu, Huterer, Joffre (2001)

44 Lens Redshifts If baryon features are detected, ultimate standard ruler! 10 0 hh l(l+1)cl /2π 10 1 z= z= l 4000deg 2, M>10 14 Cooray, Hu, Huterer, Joffre (2001)

45 Summary N-body sims and halo model for understanding power spectrum stats. and higher order correlations Likelihood analysis is feasible and near optimal for current generation of surveys

46 Summary N-body sims and halo model for understanding power spectrum stats. and higher order correlations Likelihood analysis is feasible and near optimal for current generation of surveys properly accounts for sparse or uneven sampling and correlated noise built in monitors for systematic errors techniques for marginalizing known systematics w. templates

47 Summary N-body sims and halo model for understanding power spectrum stats. and higher order correlations Likelihood analysis is feasible and near optimal for current generation of surveys properly accounts for sparse or uneven sampling and correlated noise built in monitors for systematic errors techniques for marginalizing known systematics w. templates Power spectrum complements CMB information order of magnitude increase in precision Tomography (including CMB lensing) assists in identifying dark energy (including possible clustering) Lens redshifts yield halo number counts and halo power spectra

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