Upload
alice-watkins
View
223
Download
2
Tags:
Embed Size (px)
Citation preview
R. Mandelbaum 2
CosmologyA homogeneous and isotropic
universe
Spatially flat and expanding (accelerating!)
General Relativity:Function of the metric (defining space-time behavior)
Stress-energy tensor
describes matter/energy
contents
4Picture credits: NASA/WMAP science team
Quantum fluctuations seed small (/ ~10-5) inhomogeneities…
…which are imprinted in CMB...
Matter domination: growth through gravitational instabilityR. Mandelbaum
5
Two classes of cosmological probes
Geometric: SN1A, BAO
Growth of structure
Picture credits: ESA/ESO (left), MPE/V. Springel (right)
R. Mandelbaum
R. Mandelbaum 6
Summary: current status in cosmology
An observationally supported big picture
BUT… many fundamental uncertaintiesnature of DM and DE, nature of inflationary era, GR confirmation on many scales. ?
R. Mandelbaum 7
A key problem:The universe is dominated by dark
contents.
But…we cannot directly observe those contents using a telescope.
R. Mandelbaum 14
Galaxy-galaxy lensingStacked lens galaxy position – source galaxy
shape cross-correlation
Reveals total average matter distribution around lens galaxies or cluster (galaxy-mass correlation)
R. Mandelbaum 17
Subaru telescopeMany instruments for optical and spectroscopic
observations, e.g. Suprime-Cam
Miyatake, Takada, RM, et al (2012)
R. Mandelbaum 21
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 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!
(LSST science book)
R. Mandelbaum 23
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!
Zhan et al. (2006, 2008)
R. Mandelbaum 24
A reminderCosmic 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
Position
GalaxiesDensity
Dark matter halo
R. Mandelbaum 26
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 27
That’s not the only problem, either.
Intrinsic alignments
Theoretical uncertainties on small scales (e.g. baryonic effects)
Photometric redshift uncertainties
R. Mandelbaum 28
ImplicationsAs 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 29
What data will we have?The lensing shear field: HSC
The 2d galaxy density field: HSC
(Sometimes) 3d galaxy density field and velocity field, with spectroscopy: BOSS
X-ray (galaxy clusters): XMM
SZ (galaxy clusters), CMB
lensing: ACT
Lensing magnification field?
(M. White)
R. Mandelbaum 30
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 31
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 32
Observational quantities
• ξgg from galaxy clustering
• ρξgm from g-g weak lensing
• Infer matter clustering (schematically): Constrain
nonlinear matter power spectrum on large scales
R. Mandelbaum 33
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 34
A concrete example:
Lensing + clustering in SDSS DR7
(RM, Anze Slosar, Tobias Baldauf, Uros Seljak,
Christopher Hirata, Reiko Nakajima, Reinabelle Reyes,
2012)
R. Mandelbaum 35
Observational quantities
• ξgg from galaxy clustering
• ρξgm from g-g weak lensing
• Infer matter clustering (schematically): Constrain
nonlinear matter power spectrum
Cross-correlation coefficient between galaxies, matter
R. Mandelbaum 36
Problem: small scalesTheoretical uncertainties in Σ (surface
density): Baryonic effects Cross-correlation ≠ 1 Cannot remove by avoiding small scale ΔΣ
Integration lower limit is the problem
R. Mandelbaum 37
Solution to small-scale issues
• Define “Annular differential surface density” (ADSD):
NO dependence on signal below R0!
→0 at R0
→ΔΣ at R>>R0
T. Baldauf, R. E. Smith, U. Seljak, RM, 2010, Phys. Rev. D, 81, 3531RM, U. Seljak, T. Baldauf, R. E. Smith, 2010, MNRAS, 405, 2078
R. Mandelbaum 38
Example from simulationsC
ross
-corr
ela
tion
coeff
(r c
c)
Using ΔΣ
Using ϒ, R0=3 Mpc/h
Reconstruction
ϒmm
R. Mandelbaum 40
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 41
Example of current data
Stacked data:
~105 LRGs (lenses),
70M sources
Lensing
signal
Transverse separation R (Mpc/h)
R. Mandelbaum 44
Actual procedure
Direct fitting: Nonlinear power
spectrum PT-motivated
parametrization of non-linear 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 46
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 48
But that’s not all…Small-scale lensing profiles reveal galaxy DM
halos
Transverse separation R (Mpc/h)
R. Mandelbaum 49
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 50
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 51
ModelingNeed HOD model for how galaxies populate
halos
Include variable fraction that are offset within halos, their spatial and velocity distributions
Hikage, RM, Takada, Spergel (2012)
R. Mandelbaum 52
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 53
ConclusionsCurrent 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