Cosmology with the Large Synoptic Survey Telescope

1
LSS Projects with LSST
Hu Zhan (UC Davis) For the Large-Scale Structure
Science Collaboration
2
Large-Scale Structure An Introduction
3
Large-Scale Structure Tools
SDSS DR3
Baryon Acoustic Oscillations ? standard ruler ?
dark energy
4
Large-Scale Structure A Journey
WMAP media _at_ map.gsfc.nasa.gov
5
Large-Scale Structure A Journey
Fluctuations in the early universe are imprinted
in the CMB, overdensities grow under
gravitational instability, and galaxies form in
local density peaks.
6
Projects with LSST

• Power Spectrum/2-point Correlation Function
  (galaxies, quasars, SNe, galaxy clusters)
• Baryon acoustic oscillations, distance, dark
  energy/modified gravity, wm, wb, mn, Wk, ns, as,
  primordial fluctuations (power on very-large
  scales), inflation.
• Joint Analysis of Galaxy Overdensity Weak
  Lensing Shear Maps
• S.A.A., but achieving much stronger constraints
  with the extra galaxy-shear information and
  mutual calibration of systematic uncertainties,
  intrinsic alignment, galaxy halo formation,
  halo model, baryonic effects, galaxy bias.
• Clusters (optical, WL, X-ray,
  Sunyaev?Zeldovich)
• Self-calibration of the mass-observable relation
  and the mass threshold with counts variance,
  joint analysis with WL, SZ, X-ray (SPT, Planck,
  eROSITA), halo assembly bias, Wm, s8, dark
  energy, dark matter, substructure, cluster galaxy
  evolution.
• Galaxy Cross Correlations (photo?photo,
  photo?spec, galaxy?SN, galaxy?SN mag.)
• Calibrating the photo-z error distribution,
  checks for systematics, SN magnification.
• N-Point Statistics (N gt 2) Beyond
• Galaxy bias, non-Gaussianity, nonlinear
  evolution, dark energy, galaxy formation.
• Integrated Sacks-Wolfe Effect (Correlation with
  CMB)
• Very-large-scale structure formation, dark
  energy.
• Galaxy Counts-in-Cells (beyond Cosmology)
• Galactic extinction, dust map (w/ IR).

7
Baryon Acoustic Oscillations
Imprints on the matter power spectrum (White 2005)
8
Lya Emitter Clustering in MUSYC-ECDFS
(Gawiser et al. 2007, ApJ 671, 278)
162 LAE candidates
Clustering analysis by Harold Francke
Bias evolution suggests that LAEs at z 3.1
evolve into L galaxies at z 0.
9
D(z) and G(z) from LSST BAO and WL
Zhan, Knox, Tyson, in prep

• D1 D14 from z 0.14 to 5 G0 G14 from z
  0 to 5.
• BAO distances are generally more accurate than
  WL ones.
• But WL has eigenmodes that are better determined
  than all BAO modes.
• Joint results are less sensitive to the
  systematics of each technique.

10
Complementarity between BAO and WL

• Projection of errors of distance eigenmodes onto
  w0?wa space.
• 5 WL distance eigenmodes account for most of the
  WL constraints on w0 wa.
• BAO WL are highly complementary.

11
s(wp)s(wa) Dependence on Photo-z RMS

• The error product (EP) is NOT for the LSST galaxy
  BAO, but the trend is applicable to photo-z
  angular BAO measurements.
• The impact of uncertainties in the photo-z bias
  and rms depends on the survey.
• Cross-correlations between redshift bins will
  help reduce the photo-z uncertainties.
• At large sz , the EP is roughly proportional to
  sz .
• Radial BAO information becomes available at sz lt
  0.01(1 z).

Zhan et al. (2008)
12
Improved Galaxy Spectral Templates
http//www.ice.csic.es/personal/jimenez/PHOTOZ/ind
ex.html
New, physically motivated, high-resolution
templates for photo-zs.
(Niemack et al. 2008)
Application to SDSSGALEX
From Licia Verde
13
Photo-z Calibration with Cross Correlations
Photo-z distribution in red, spectroscopic
samples in green, blue, yellow. The
cross-correlations between the photo-z sample and
spectroscopic samples increase as the photo-z
distribution peaks. This, together with the
auto-correlations of each sample, can be used to
calibrate the photo-z distribution (Newman 2008,
0805.1409).
14
Photo-z Calibration with Cross Correlations
Photometric redshift bins
Kernel ? galaxy distribution in true-redshift
space
Photo-z?photo-z cross correlations will also help
calibrate the photo-z error distribution (Zhan
2006 Schneider et al. 2006).
15
Primordial Non-Gaussianity
Large-scale clustering of halos is a good probe
of primordial non-Gaussianity and hence
inflation. The effect of NG appears at large
scales. As for BAOs surveys, large volumes need
to be covered. Tests on simulations and forecast
errors is on going.
fNL 100
(Mpc-1)
F f fNL (f2 - f2) WMAP results (Komatsu
et al. 2008) -9 lt fNLlocal lt 111 -151 lt
fNLequil lt 253 (95 CL)
Matarrese Verde (2008) Carbone et al. (2008)
Grossi et al. (2008)
From Licia Verde
16
Cluster Counting
tCDM vs. LCDM (Evrard et al. 2002)
Dark energy sensitivity(Mohr 2004)
With redshifts
No redshift
Sensitive to both cosmic expansion and growth
histories, but also prone to errors in the
mass-observable relation. Multi-wavelength
observations are helpful. See also
self-calibrations Lima Hu (2004, 2005)
Majumdar Mohr (2004)
Planck SZ clusters
Importance of redshifts (Geisbusch Hobson 2007)
17
SN Magnification?Galaxy Correlation
The effect should be there and is useful for
cross-check. However, it is hard to detect for
individual SNe. LSST can improve the detection
with angular correlation between SN residual
magnitudes and foreground galaxy over-densities.
SN residual magnitude vs. expected magnification
from foreground galaxy distribution.
This is an example of synergy between LSST LSS,
SN, WL Science Collaborations.
Tentative detection of the gravitational
magnification of Type Ia SNe (Jonsson et al.
2006, 2007)
Suggested by Asantha Cooray
18
Projects with LSST

• Power Spectrum/2-point Correlation Function
  (galaxies, quasars, SNe, galaxy clusters)
• Baryon acoustic oscillations, distance, dark
  energy/modified gravity, wm, wb, mn, Wk, ns, as,
  primordial fluctuations (power on very-large
  scales), inflation.
• Joint Analysis of Galaxy Overdensity Weak
  Lensing Shear Maps
• S.A.A., but achieving much stronger constraints
  with the extra galaxy-shear information and
  mutual calibration of systematic uncertainties,
  intrinsic alignment, galaxy halo formation,
  halo model, baryonic effects, galaxy bias.
• Clusters (optical, WL, X-ray,
  Sunyaev?Zeldovich)
• Self-calibration of the mass-observable relation
  and the mass threshold with counts variance,
  joint analysis with WL, SZ, X-ray (SPT, Planck,
  eROSITA), halo assembly bias, Wm, s8, dark
  energy, dark matter, substructure, cluster galaxy
  evolution.
• Galaxy Cross Correlations (photo?photo,
  photo?spec, galaxy?SN, galaxy?SN mag.)
• Calibrating the photo-z error distribution,
  checks for systematics, SN magnification.
• N-Point Statistics (N gt 2) Beyond
• Galaxy bias, non-Gaussianity, nonlinear
  evolution, dark energy, galaxy formation.
• Integrated Sacks-Wolfe Effect (Correlation with
  CMB)
• Very-large-scale structure formation, dark
  energy.
• Galaxy Counts-in-Cells (beyond Cosmology)
• Galactic extinction, dust map (w/ IR).