Non-Gaussian signatures in cosmic shear fields Masahiro Takada (Tohoku U., Japan) Oct 26th 07 @ ROE...
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Transcript of Non-Gaussian signatures in cosmic shear fields Masahiro Takada (Tohoku U., Japan) Oct 26th 07 @ ROE...
Non-Gaussian signatures in Non-Gaussian signatures in cosmic shear fieldscosmic shear fields
Masahiro Takada
(Tohoku U., Japan)
Oct 26th 07 @ ROE
Based on collaboration with
Bhuvnesh Jain (Penn) (MT & Jain 04, MT & Jain 07 in prep.)
Sarah Bridle (UCL) (MT & Bridle 07, astro-ph/0705.0163)
Some part of my talks is based on the discussion of WLWG
Outline of this talkOutline of this talk
• What is cosmic shear tomography?
• Non-Gaussian errors of cosmic shear fields and the higher-order moments
• Parameter forecast including non-Gaussian errors
• Combining WLT and cluster counts
• Summary
Cosmological weak lensing – cosmic shearCosmological weak lensing – cosmic shear
present
z=zs
z=zl
z=0
past
Large-scale structureϕγγϕγγ
γ
2sin
2cos
2
1
=
=+
−=
ba
baobservables
• Arises from total matter clustering– Not affected by galaxy bias uncertainty
– well modeled based on simulations (current accuracy, <10% White & Vale 04)
• A % level effect; needs numerous (~108) galaxies for the precise measurements
Weak Lensing Tomography Weak Lensing Tomography
• Subdivide source galaxies into several bins based on photo-z derived from multi-colors (e.g., Massey etal07)
• <zi> in each bin needs accuracy of ~0.1%
• Adds some ``depth’’ information to lensing – improve cosmological paras (including DE)
+ m(z)
(e.g., Hu 99, 02, Huterer 01, MT & Jain 04)
Tomographic Lensing Power SpectrumTomographic Lensing Power Spectrum
• Tomography allows to extract redshift evolution of the lensing power spectrum.
• A maximum multipole used should be like l_max<3,000
Tomographic LensiTomographic Lensing Power Spectrum ng Power Spectrum
(contd.)(contd.)
• Lensing PS has a less feature shape, not like CMB– Can’t better constrain inflation parameters (n_s and alp
ha_s) than CMB– Need to use the lensing power spectrum amplitudes t
o do cosmology: the amplitude is sensitive to A_s, de0
(or m0), w(z).
),()(
)(),(00 èL
SS
LLSLLSz
Lm zzd
zdzzddz
S γ ∫∝
Lenisng tomography (condt.)Lenisng tomography (condt.)
• WLT can be a powerful probe of DE energy density and its redshift evolution.• Need 3 z-bins at least, if we want to constrain DE model with 3 parameters (_
de,w0, wa)• Less improvement using more than 4 z-bins, for the 3 parameter DE model
An example of survey parameters An example of survey parameters (on a behalf of HSCWLWG)(on a behalf of HSCWLWG)
• PS measurement error (survey area)^-1• Requirements: expected DE constraints should be comparable wit
h or better than those from other DE surveys in same time scale (DES, Pan-Starrs, WFMOS)
• Note: optimization of survey parameters are being investigated using the existing Suprime-Cam data (also Yamamoto san’s talk)
222
1)12(
2)(⎟⎟⎠
⎞⎜⎜⎝
⎛+
Δ+=⎟⎟
⎠
⎞⎜⎜⎝
⎛
lgskyl
l
CnlflCC εσσ
Area: ~2,000 deg^2
Filters: B~26,V~26,R~26, i’~25.8, z’~24.3
Nights: 150-300 nights
Non-linear clusteringNon-linear clustering
• Most of WL signal is from small angular scales, where the non-linear clustering boosts the lensing signals by an order of magnitude (Jain & Seljak97).
• Large-scale structures in the non-linear stage are non-Gaussian by nature.
• 2pt information is not sufficient; higher-order correlations need to be included to extract all the cosmological information
• Baryonic physics: l>10^3
Non-linear clustering
l_max~3000
Non-Gaussianity induced by structure formationNon-Gaussianity induced by structure formation• Linear regime O()<<1; all the Fourier modes of the
perturbations grow at the same rate; the growth rate D(z)– The linear theory, based on FRW + GR, gives robust, secure predictions
• Mildly non-linear regime O()~1; a mode coupling between different Fourier modes is induced– The perturbation theory gives the specific predictions for a CDM model
• Highly non-linear regime; a more complicated mode coupling– N-body simulation based predictions are needed (e.g., halo model)
)1000()()( == zzDz kk
0)()(
)(),(
)(
4)1()2(2)1(3
21)1()1(
2123
13)2(
)3()2()1(
21
≠∝∝⇒
−−=
+++=
∫∫
kkkkkkk kkk Fdd
z L
,....], ;),([),( LNLbmi zkPfzkP =
• Correlations btw density perturbations of different scales arisCorrelations btw density perturbations of different scales arise as a consequence of non-linear structure formation, originate as a consequence of non-linear structure formation, originating from the initial Gaussian fieldsing from the initial Gaussian fields
• However, the non-Gaussianity is fairly accurately predictable However, the non-Gaussianity is fairly accurately predictable based on the CDM modelbased on the CDM model
Aspects of non-Gaussianity in cosAspects of non-Gaussianity in cosmic shearmic shear
• Cosmic shear observables are non-Gaussian– Including non-Gaussian errors degrades the cosmologic
al constraints?– Realize a more realistic ability to constrain cosmologic
al parameters– The dependences for survey parameters (e.g., shallow s
urvey vs. deep survey)
• Yet, adding the NG information, e.g. carried by the bispectrum, is useful?
Covariance matrix of PS measurementCovariance matrix of PS measurement
• Most of lensing signals are from non-linear scales: the errors are non-Gaussian
• PS covariance describes correlation between the two spectra of multipoles l1 and l2 (Cooray & Hu 01), providing a more realistic estimate of the measurement errors
• The non-Gaussian errors for PS arise from the 4-pt function of mass fluctuations in LSS
€
Cov[P(l1),P(l2)] = P(l1),P(l2) − P(l1)P(l2)
= −
=δl1l2
fsky l1Δl1P(l1) +
σ ε2
ng
⎡
⎣ ⎢
⎤
⎦ ⎥
2
+1
4πfsky
d2l1'
2πl1Δl1l1∫ d2l2
'
2πl2Δl2l2
∫ T(l1',−l1
',l2' ,−l2
' )
l1l2
l1l2
l2l1
l1 l2
Gaussian errors
Non-Gaussian errors
(MT & Jain 07 in prep.)
Correlation coefficients of PS cov. matrixCorrelation coefficients of PS cov. matrix
],[Cov],[Cov
],[Cov
jjii
jiij
PPPP
PPr =
• Diagonal: Gaussian Off-diagonal: NG, 4-pt function
• 30 bins: 50<l<3000• If significant
correlations, r_ij1• The NG is stronger at
smaller angular scales• The shot noise only
contributes to the Gaussian (diagonal) terms, suppressing significance of the NG errors
w/o shot noise
with shot noise
Correlations btw Correlations btw Cl’Cl’s at different s at different l’l’ss
• Principal component decomposition of the PS covariance matrix
€
SiaC(li){ } S jbC(l j ){ } = λ aδab
Power spectrum with NG errorsPower spectrum with NG errors
• The band powers btw different ells are highly correlated (also see Kilbinger & Schneider 05)
• NG increases the errors by up to a factor of 2 over a range of l~1000
• ell<100, >10^4, the errors are close to the Gaussian cases
(in z-space as well for WLT)
Signal-to-noise ratio: SNRSignal-to-noise ratio: SNR• Data vector: power spectra binned in multipole range, l_
min<l<l_max, (and redshifts)
• In the presence of the non-Gaussian errors, the signal-to-noise ratio for a power spectrum measurement is
• For a larger area survey (f_sky ) or a deeper survey (n_g ), the covariance matrix gets smaller, so the signal-to-noise ratio gets increased; S/N €
S
N
⎛
⎝ ⎜
⎞
⎠ ⎟2
= Di Cov Pnm (l),Pn'm '(l')( )[ ]ij
−1D j
i, j=1
n
∑
€
D = P11(l1),P12(l2),L ,P(ns −1)ns(ln−1),Pns ns
(ln ){ }
Signal-to-noise ratio: SNR (contd.)Signal-to-noise ratio: SNR (contd.)
• Multipole range: 50<l<l_max
• Non-gaussian errors degrade S/N by a factor of 2
• This means that the cosmic shear fields are highly non-Gaussian (Cooray & Hu 01; Kilbinger & Schneider 05)
GaussianNon-Gaussian
50<l<l_max
The impact on cosmo para errorsThe impact on cosmo para errors
_de
w_0
w_a
n_s
….
_mh^2
_bh^2
• We are working in a multi-dimensional parameter space (e.g. 7D)
error ellipse _de
w_0
w_a
n_s
….
_mh^2
_bh^2
Non-Gaussian Error
• Volume of the ellipse: VNG2VG
• Marginalized error on each parameter length of the principal axis: σNG ~ 2^(1/Np)σG (reduced by the dim. of para space)– Each para is degraded by slightly different amounts
– Degeneracy direction is slightly changed
1l2l
3l
1l2l
3l
1l2l
3l
l
An even more direct use of NAn even more direct use of NG: bispectrumG: bispectrum
:)( 22)()()( κκ PWlC GLijji ⇐⇒
:),,( 43321)()()()( κκκ PWB GLijkkji ⇐⇒ lll given as a function of triangles
given as a function of separation l
1l2l
3l
l
1sz2sz
Bernardeau+97, 02, Schneider & Lombardi03, Zaldarriaga & Scoccimarro 03, MT & Jain 04, 07, Kilbinger & Schneider 05
A more realistic parameter forecastA more realistic parameter forecastMT & Jain in prep. 07
WLT (3 z-bins) + CMB
• Parameter errors: PS, Bisp, PS+Bisp – G: σ(_de)=0.015, 0.014, 0.010 NG: 0.016(7%), 0.022(57), 0.013(30) – σ(w0)= 0.18, 0.20, 0.13 0.19(6%), 0.29(45), 0.15(15) – σ(wa)= 0.50, 0.57, 0.38 0.52(4%), 0.78(73), 0.41(8)
• The errors from Bisp are more degraded than PS– Need not go to 4-pt!
• In the presence of systematics, PS+Bisp would be very powerful to discriminate the cosmological signals (Huterer, MT+ 05)
WLT + Cluster CountsWLT + Cluster Counts
• Clusters are easy to find from WL survey itself: mass peaks (Miyazaki etal.03; see Hamana san’s talk for the details)
• Synergy with other wavelength surveys (SZ, X-ray…) – Combining WL signal and other data is very useful to discriminate real clu
sters from contaminations
• Combing WL with cluster counts is useful for cosmology?– Yes, would improve parameter constraints, but how complementary?
• Cluster counts is a powerful probe of cosmology, established method (e.g., Kitayama & Suto 97)
MT & S. Bridle astro-ph/0705.0163
)obs;()( 2
cl mSmndmdzd
VddzN ∫∫
=Angular number counts:
w0=-1 w0=-0.9
Mass-limited cluster counts vs. lenMass-limited cluster counts vs. lensing-selected countssing-selected counts
• Mass-selected sample (SZ) vs lensing-based sample
Halo distribution Convergence map
Hamana, MT, Yoshida 04
2 d
egre
es
Miyazaki, Hamana+07
QuickTime˛ Ç∆TIFFÅià≥èkǻǵÅj êLí£ÉvÉçÉOÉâÉÄ
ǙDZÇÃÉsÉNÉ`ÉÉÇ å©ÇÈÇΩÇflÇ…ÇÕïKóvÇ≈Ç∑ÅB
Mass
Light (galaxies)
X-ray
Secure candidates
A closer look at nearby clusters (z<0.3)A closer look at nearby clusters (z<0.3)
QuickTime˛ Ç∆TIFFÅià≥èkǻǵÅj êLí£ÉvÉçÉOÉâÉÄ
ǙDZÇÃÉsÉNÉ`ÉÉÇ å©ÇÈÇΩÇflÇ…ÇÕïKóvÇ≈Ç∑ÅB
~30 clusters (Okabe, MT, Umetsu+ in prep.)
• Subaru is superb for WL measurement• A detailed study of cluster physics (e.g. the nature of dark matter)
Cross-correlation between CC and WLCross-correlation between CC and WL
• If the two observables are drawn from the same survey region, the two probe the same cosmic mass density field in LSS
• Around each cluster, stronger shear signal is expected: up to ~10% in induced ellipticities, compared to a few % for typical cosmic shear
• A positive cross-correlation is expected: Clusters happen to be less/more populated in a given survey region than expected, the amplitudes of <γγ> are most likely to be smaller/greater
• Note that < γγ >: 2pt, cluster counts (CC): 1pt =>no correlation for Gaussian fields
A patch of the observed sky
Cluster
Shearing effect on background galaxies
Cross-correlation btw CC and WL (contd.)Cross-correlation btw CC and WL (contd.)
• Shown is the halo model prediction for the lensing power spectrum
• A correlation between the number of clusters and the ps amplitude at l~10^3 is expected.
M/M_s>10^14
M/M_s>10^13
M/M_s>10^15
Cross-covariance between CC + WLCross-covariance between CC + WL• Cross-covariance between PS binned in l and z and the cluster c
ounts binned in z
• The cross-correlation arises from the 3-pt function of the cluster distribution and the two lensing fields of background galaxies– The cross-covariance is from the non-Gaussianity of LSS
• The structure formation model gives specific predictions for the cross-covariance
SNR for SNR for CC+WLCC+WL
• The cross-covariance leads to degradation and improvement in S/N up to ~20%, compared to the case that the two are independent
Parameter forecasts for CC+WLParameter forecasts for CC+WL
• Lensing-selected sample with detection threshold S/N~10 contains clusters with >10^15Msun
• Lensing-selected sample is more complementary to WLT, than a mass-selected one? Needs to be more carefully addressed
lensing-selected sample mass-selected sample
WLCC+WLCC+WL with Cov
HSCWLS performance (WLT+CC+CMBHSCWLS performance (WLT+CC+CMB) )
• Combining WLT and CC does tighten the DE constraints, due to their different cosmological dependences
• Cross-correlation between WLT and CC is negligible; the two are considered independent approximately
Real world: issues on systematic errorsReal world: issues on systematic errors• E/B mode separation as a diagnostics of systematics• Non-gaussian signals in weak lensing fields• Theoretical compelling theoretical modeling of DE• Shape measurement accuracies vs. galaxy types, morphology, magnitudes…• Data reduction pipelines optimized for weak lensing analyses• Exploring a possibility to self-calibrate systemtaics, by combining different methods• Non-linearities in lensing; reduced shear needs to be included?• Intrinsic alignments• Source clustering, source-lens coupling• Usefulness of Flexions?• Develop a sophisticated photo-z code• Photo-z vs. color space? • Requirement on spec-z sub-sample; from which data?• N-body simulations (initial conditions, how to work in multi-dimenaional parameter space for N-body simulations, the
strategy…)• DE vs. modified gravity• Fourier space vs. real-space; explore an optimal method to measure power spectrum from actual data, with complex su
rvey geometry• Exploring a code of likelihood surface in a multi-dimensional parameter space (MCMC); how to combine with other p
robes such as CMB, 2dF/SDSS, ….• Can measure DE clustering or neutrino mass from WL or else with HSC?• Defining survey geometry: a given total survey area, many small-patched survey regions vs. continuous survey region• Adding multi-color info for WL based cluster finding; color properties of member ellipticals would be useful to discri
minate real lensing mass peaks as well as know the redshift • How to calibrate mass-observable relation for cluster experiments? WL + colors + SZ + X-ray?• Constraining mass distribution within a cluster with HSC WL survey; mass profile, halo shape, etc• Strong lens statistics• Imaging BAO• Man power problem: who and when to work on these issues? • …
Issues on systematics: self-calibrationIssues on systematics: self-calibration
,..... , 222111 jjjiii SCOSCO +=+=
jijiji CCOOOO 212121 ],[Cov ==
• If several observables (O1,O2,…) are drawn from the same survey region: e.g., WLPS, WLBisp, CC,…– Each observable contains two contributions (C: cosmological signal and
S: systematics)
• Covariances (or correlation) between the different obs.– If the systematics in different obs are uncorrelated
– The cosmological covariances are fairly accurately predictable
• Taking into account the covariances in the analysis could allow to discriminate the cosmological signals from the systemacs – self-calibration– Working in progress
SummarySummary• The non-Gaussian errors in cosmic shear fields arise from non
-linear clustering in structure formation– The CDM model provides the specific predictions, so the NG errors ar
e in some sense accurately predictable
• Bad news: the NG errors are very important to be included for current and, definitely, future surveys– The NG degrades the S/N for the lensing power spectrum measuremen
t up to a factor of 2
• Good news: the NG impact on cosmo para errors are less significant if working in a multi-dimensional parameter space– ~10% for 7-D parameter space
• WLT and cluster counts, both available from the same imaging survey, can be used to tighten the cosmological constraints