Report

The next decade of weak lensing science Rachel Mandelbaum, CMU Cosmology A homogeneous and isotropic universe Spatially flat and expanding (accelerating!) General Relativity: Function of the metric (defining space-time behavior) R. Mandelbaum Stress-energy tensor describes matter/energy contents 2 ????? Name for model: CDM R. Mandelbaum ?? Picture credits: NASA/WMAP science team 3 Quantum fluctuations seed small (/ ~10-5) inhomogeneities… …which are imprinted in CMB... Matter domination: growth through gravitational instability R. Mandelbaum Picture credits: NASA/WMAP science team 4 Two classes of cosmological probes Geometric: SN1A, BAO R. Mandelbaum Growth of structure Picture credits: ESA/ESO (left), MPE/V. Springel (right) 5 Summary: current status in cosmology An observationally supported big picture BUT… many fundamental uncertainties nature of DM and DE, nature of inflationary era, GR confirmation on many scales. R. Mandelbaum 6 A key problem: The universe is dominated by dark contents. But…we cannot directly observe those contents using a telescope. R. Mandelbaum 7 Gravitational lensing Lensing deflection of light: 8 R. Mandelbaum Sensitive to all matter along line of sight, including dark matter! 9 Weak lensing Unlensed R. Mandelbaum Lensed 10 Galaxies aren’t really round NASA, ESA, S. Beckwith (STScI) and the HUDF Team Cosmic shear Shape autocorrelation statistical map of large-scale structure R. Mandelbaum 13 Galaxy-galaxy lensing Stacked lens galaxy position – source galaxy shape cross-correlation Reveals total average matter distribution around lens galaxies or cluster (galaxy-mass correlation) R. Mandelbaum 14 State of the field of weak lensing mid-1990s: first detections of weak lensing 2000-2001: first cosmological weak lensing measurements (~20-30% errors) 2002-2012: increasingly precise lensing measurements in a variety of datasets, down to ~5-10% errors 2013: several large, ground-based surveys will start measuring lensing with unprecedented precision (~2%) ~2020: 2 surveys will start to measure lensing with sub-% precision R. Mandelbaum 15 Subaru telescope 8.2 meter primary mirror Mauna Kea Excellent imaging conditions R. Mandelbaum 16 Subaru telescope Many instruments for optical and spectroscopic observations, e.g. Suprime-Cam R. Mandelbaum Miyatake, Takada, RM, et al (2012) 17 Picture credit: S. Miyazaki R. Mandelbaum 18 HSC is on the telescope! HSC blog at naoj.org R. Mandelbaum 19 Looking good! R. Mandelbaum 20 3-layer HSC survey Wide: ~1400 deg2, i<25.8 (grizy) Weak lensing, z<1.5 galaxy populations Deep: ~26 deg2, 1 mag deeper, 5 wide+3 NB filters Ly-α emitters, quasars, deeper galaxy populations, lensing systematics, … Ultradeep: 3 deg2, 1 mag deeper, 5 wide+6 NB filters Supernovae, galaxies to z<7 Important synergies: CMB (ACT+ACTPol), redshifts (BOSS + assorted other), NIR, u band, … R. Mandelbaum 21 What has driven this development? ~8-10 years ago, people started to realize how very powerful cosmic shear is as a probe of dark energy! R. Mandelbaum (LSST science book) 22 What has driven this development? ~8-10 years ago, people started to realize how very powerful cosmic shear is as a probe of dark energy! R. Mandelbaum Zhan et al. (2006, 2008) 23 A reminder Cosmic shear measures the matter power spectrum This is easily predicted from theory (modulo small-scale effects) Contrast: the galaxy power spectrum from redshift surveys – galaxies are a biased tracer of matter Galaxies Dark matter halo Density Position R. Mandelbaum 24 BUT R. Mandelbaum 25 This is actually kind of difficult. Cosmic shear is an auto-correlation of shapes: Multiplicative biases are an issue! Coherent additive biases become an additional term! R. Mandelbaum 26 That’s not the only problem, either. Intrinsic alignments Theoretical uncertainties on small scales (e.g. baryonic effects) Photometric redshift uncertainties R. Mandelbaum 27 Implications As datasets grow, our control of systematics must get increasingly better The past ~3 years have seen a change of perspective within the lensing community: We should measure cosmic shear But we should also identify combinations of lensing measurements with other measurements that allow us to calibrate out / marginalize over systematics directly Use ALL the information available Minimize the combination of statistical + systematic error! R. Mandelbaum 28 What data will we have? The lensing shear field: HSC The 2d galaxy density field: HSC (Sometimes) 3d galaxy density field and velocity field, (M. White) with spectroscopy: BOSS X-ray (galaxy clusters): XMM SZ (galaxy clusters), CMB lensing: ACT Lensing magnification field? R. Mandelbaum 29 Summary of approach to future data: Cross-correlate everything with everything = more information = less sensitivity to observational uncertainties specific to one particular method R. Mandelbaum 30 What about galaxy-galaxy lensing? Typically undervalued for cosmology, because it measures gm correlations, not mm Observationally easier: Coherent additive shear errors do not contribute at all! (cross-correlation) Intrinsic alignments: Don’t enter at all, with robust lens-source separation If sources are not well behind lenses, they contribute, but in a different way from cosmic shear R. Mandelbaum 31 Observational quantities • gg from galaxy clustering • gm from g-g weak lensing • Infer matter clustering (schematically): Constrain nonlinear matter power spectrum on large scales R. Mandelbaum 32 Let’s include cosmic shear Use cosmic shear (mm), galaxy-galaxy lensing (gm), and galaxy clustering (gg) Dependence on intrinsic alignments, shear systematics: Different for the two lensing measurements Joachimi & Bridle 2011, Kirk et al. (2011), Laszlo et al. (2011) showed that the cosmological power is = that of cosmic shear, even when marginalizing over extensive models for systematics! R. Mandelbaum 33 A concrete example: Lensing + clustering in SDSS DR7 (RM, Anze Slosar, Tobias Baldauf, Uros Seljak, Christopher Hirata, Reiko Nakajima, Reinabelle Reyes, 2012) R. Mandelbaum 34 Observational quantities • gg from galaxy clustering • gm from g-g weak lensing • Infer matter clustering (schematically): Constrain nonlinear matter power spectrum R. Mandelbaum Cross-correlation coefficient between galaxies, matter 35 Problem: small scales Theoretical uncertainties in Σ (surface density): Baryonic effects Cross-correlation ≠ 1 Cannot remove by avoiding small scale ΔΣ R. Mandelbaum Integration lower limit is the problem 36 Solution to small-scale issues • Define “Annular differential surface density” (ADSD): →0 at R0 →ΔΣ at R>>R0 NO dependence on signal below R0! T. Baldauf, R. E. Smith, U. Seljak, RM, 2010, Phys. Rev. D, 81, 3531 RM, U. Seljak, T. Baldauf, R. E. Smith, 2010, MNRAS, 405, 2078 R. Mandelbaum 37 Cross-correlation coeff (rcc) Example from simulations R. Mandelbaum Using ΔΣ Reconstruction Using ϒ, R0=3 Mpc/h ϒmm 38 Sensitivity to cosmology Fiducial cosmology: Ωm=0.25 σ8=0.8 ns=1.0 R. Mandelbaum 39 Results Lenses: SDSS-I spectroscopic samples: LRGs, z~0.3, typically 3L*, ~105 Main, z~0.1, typically L*, 6 × 105 Sources: 6 × 107 fainter galaxies Treat samples separately, for sanity checks Updated treatment of lensing systematics (RM et al. 2011, Reyes et al. 2011) R. Mandelbaum 40 Example of current data Stacked data: ~105 LRGs (lenses), 70M sources Lensing signal Transverse separation R (Mpc/h) R. Mandelbaum 41 Lensing data R. Mandelbaum 42 Clustering data R. Mandelbaum 43 Actual procedure Direct fitting: Nonlinear power spectrum PT-motivated parametrization of nonlinear bias With these data alone, fitting for σ8, Ωm, and bias, marginalizing over bias and lensing calibration: σ8 (Ωm/0.25)0.57 = 0.80±0.05 R. Mandelbaum 44 Non-flat, free wde R. Mandelbaum 45 Comparison to cosmic shear results COSMOS (Schrabback et al. 2010), 11% σ8 constraint CFHTLenS (Kilbinger et al. 2012), 4% σ8 constraint Typical z~1, 0.8 vs. 0.25 for SDSS SDSS gives better control of redshift systematics Results shown here establish SDSS among the most competitive extant surveys for weak lensing cosmology! R. Mandelbaum 46 Near future improvements BOSS + HSC: Less dominated by lensing statistical errors R. Mandelbaum 47 But that’s not all… Small-scale lensing profiles reveal galaxy DM halos R. Mandelbaum Transverse separation R (Mpc/h) 48 Example of how we can use this: FoG • Small-scale effect due to velocity dispersion within halos • Cannot simply eliminate by using only individual halos, unless chosen “center” is really at center White et al. (2011): contours of 3d correlation function R. Mandelbaum 49 Idea for how to calibrate out FoG Hikage, Takada, Spergel (2011) Rely on spectroscopic / photometric survey synergy Select halos, then compare several measurements for different choices of halo centers: Redshift-space power spectra Galaxy-galaxy lensing (matter distribution) Photometric galaxy cross-correlation R. Mandelbaum 50 Modeling Hikage, RM, Takada, Spergel (2012) Need HOD model for how galaxies populate halos Include variable fraction that are offset within halos, their spatial and velocity distributions R. Mandelbaum 51 The punch line 40% (70%) of bright (faint) LRGs are actually off-centered satellites Typical off-centering radius of 400 kpc/h Typical velocity dispersion: 500 km/s R. Mandelbaum 52 Conclusions Current g-g lensing measurements: Test theory predictions for galaxy-DM relationship Constrain cosmological parameters at various redshifts Lensing is the ONLY technique that directly probes the total matter distribution! Future datasets: better S/N cosmologically interesting powerful constraints on growth of structure, done optimally via combination of multiple observables R. Mandelbaum 53