The Inconsistent Universeresearch.ipmu.jp/seminar/sysimg/seminar/2215.pdf · 2019-03-20 · The...
Transcript of The Inconsistent Universeresearch.ipmu.jp/seminar/sysimg/seminar/2215.pdf · 2019-03-20 · The...
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The Inconsistent UniverseProblems with KiDS, or with ΛCDM?
Benjamin Joachimi (UCL)[email protected]
with Fabian Koehlinger & the KiDS Weak Lensing team
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The Inconsistent Universe B. Joachimi
KiDS
Kilo Degree Survey• on the 2.6m VLT Survey Telescope• aim: ~1400 deg2 (spring 2019)• ugri + zYJHK (VIKING)• prioritised overlap with GAMA Survey• ESO Public Survey: DR4 now public• current papers based on 450 deg2
credit: Wright
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The Inconsistent Universe B. Joachimi
Weak lensing in a nutshellNASA/ESA
If the signal is weak,need averaging over many source images.
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The Inconsistent Universe B. Joachimi
Shear measurement
Kuijken+ (2019)
(Miller et al. 2013)
• likelihood fitting of galaxy model with
lensfit
• fit ellipticity, centroid, flux, size,
bulge-to-disc ratio
GREAT08
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The Inconsistent Universe B. Joachimi
KiDS DR3
The typical weak lensing galaxy
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The Inconsistent Universe B. Joachimi
KiDS DR3
The typical weak lensing galaxy
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The Inconsistent Universe B. Joachimi
KiDS DR3
The typical weak lensing galaxy
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The Inconsistent Universe B. Joachimi
KiDS DR3
The typical weak lensing galaxy
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The Inconsistent Universe B. Joachimi
Shear calibration
Kannawadi+ (2019)
multiplicativeshear bias
real VST vs. COSMOS emulation
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The Inconsistent Universe B. Joachimi
Measurements
• best-fit cosmological model incl. intrinsic alignments and baryon feedback
• errors from fully analytic covariance
Hildebrandt+ (2019)
ξ+ ξ-
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The Inconsistent Universe B. Joachimi
S8=σ8√Ωm /0.3
The state of the art for cosmic shear
measures constraints across the banana
Hildebrandt+ (2019)
2.3σ0.9σ
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The Inconsistent Universe B. Joachimi
Redshift calibration techniques
Hildebrandt+ (2019) reweighting spec-zreweighting, smoothed over LSSCOSMOS-30 photo-zclustering cross-correlations (small-scale)clustering cross-correlations (large-scale)
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The Inconsistent Universe B. Joachimi
The importance of redshift calibration
Hildebrandt+ (2019)
0.6σ
using COSMOS-30 photo-z[DES and HSC rely on this]
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The Inconsistent Universe B. Joachimi
Could it be systematics?
Hildebrandt+ (2019)
cosmic variance in photo-z
photo-z calibration technique
no photo-z uncertaintyintrinsic alignment modelling
baryon feedback modellingno systematics
neutrino modellingexcluding redshift bins
covariance modelling
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The Inconsistent Universe B. Joachimi
Johnston+ (2019)
Intrinsic alignments of GAMA galaxies
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The Inconsistent Universe B. Joachimi
Johnston+ (2019)
Intrinsic alignments: colour dichotomy
dependence on colour/type dependence on luminosity/mass
dependence on redshift
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The Inconsistent Universe B. Joachimi
The case for joint probes analysis
Joint clustering/weak lensing analysis enables self-calibration ofintrinsic alignments, galaxy bias, n(z) uncertainties, etc.Bernstein (2009); BJ & Bridle (2010)
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The Inconsistent Universe B. Joachimi
Joint probes analysis setup
van Uitert, BJ, et al. (2018)
cosmic shear
clustering
galaxy-galaxylensing
• derive band power spectra as integrals over correlation functions• joint analytic covariance, verified on N-body simulations• same model as KiDS-450 + linear effective galaxy bias
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The Inconsistent Universe B. Joachimi
Current joint probes results
and differ by 1.4σ butare quasi-independent
KiDS-450 (real space)Cosmic shear only (power spectrum)
Clustering & galaxy-galaxy lensingJoint large-scale structure
Planck
uses spectroscopic sample forclustering (from GAMA survey)
uses photometric LRG sample forclustering (internal red sequence finder)
DES Y1 (2018)
van Uitert, BJ+ (2018)
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The Inconsistent Universe B. Joachimi
Fidelity of redshift distributionssh
ifts
in th
e to
mog
raph
ic r
edsh
ift d
istr
ibut
ion
• joint analysis prefers shift of bin 3• IA amplitude surprisingly high• … and correlated with n(z)
van Uitert, BJ+ (2018)
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The Inconsistent Universe B. Joachimi
Extended model – Horndeski gravity
Spurio Mancini, Koehlinger, BJ+ (2019)
: Planck mass run rate: braiding ↔ fifth force
scaling with
αMαB
ΩΛ(a)
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The Inconsistent Universe B. Joachimi
Joudaki+ (2018)
KiDS x spectroscopic clustering
quadrupole power spectrum:includes redshift-space distortions
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The Inconsistent Universe B. Joachimi
Is there a problem?
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The Inconsistent Universe B. Joachimi
Efstathiou & Lemos (2018)
data points
1+2σ contours aroundbest fit to remaining data
?
Are these measurements inconsistent?
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The Inconsistent Universe B. Joachimi
3 tiers of consistency checks
1. Global summary statistic
→ Bayes factor
2. Posterior-level check
→ pdfs of difference in duplicated parameters
3. Data domain check
→ proxy for posterior predictive distributions
• use a Bayesian formalism
• designed for correlated datasets
• analytic solutions for Gaussian data
• intuitive tension definitions
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The Inconsistent Universe B. Joachimi
Tier 1: Bayes factor
• split the dataset
• assign one parameter set to each split
→ prior dependence
evidenceHandley & Lemos (2019)
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The Inconsistent Universe B. Joachimi
3 tiers of consistency checks
1. Global summary statistic
→ Bayes factor
2. Posterior-level check
→ pdfs of difference in duplicated parameters
3. Data domain check
→ proxy for posterior predictive distributions
• use a Bayesian formalism
• designed for correlated datasets
• analytic solutions for Gaussian data
• intuitive tension definitions
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The Inconsistent Universe B. Joachimi
• split the dataset
• assign one parameter set to each split
• do likelihood analysis
• derive posterior of parameter differences
Tier 2: posterior-level check
Koehliner, BJ+ (2019)
simulated
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The Inconsistent Universe B. Joachimi
3 tiers of consistency checks
1. Global summary statistic
→ Bayes factor
2. Posterior-level check
→ pdfs of difference in duplicated parameters
3. Data domain check
→ proxy for posterior predictive distributions
• use a Bayesian formalism
• designed for correlated datasets
• analytic solutions for Gaussian data
• intuitive tension definitions
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The Inconsistent Universe B. Joachimi
Tier 3: data domain check
Posterior
Predictive
Distribution
Translated
Posterior
Distribution
realdata
syntheticdata
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The Inconsistent Universe B. Joachimi
• obtain model prediction from each posterior sample
• plot mode and scatter of resulting distribution (TPD)
Tier 3: data domain check
original dataTPD from joint modelTPD from split model (Bin 3)TPD from split model (\Bin 3)
black vs blue:→ goodness of fit
blue vs red:→ consistency
Koehliner, BJ+ (2019)
simulated
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The Inconsistent Universe B. Joachimi
Problems with KiDS?
No.
real data
real data
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The Inconsistent Universe B. Joachimi
Conclusions
• Current weak lensing surveys yield a consistent picture of low-z large-scale structure.
• At face value there is still a 1% chance the KiDS-Planck discrepancy is a random fluctuation.
• No known weak lensing systematics can drive the discrepancy, with the possible exception of the photometric redshift calibration.
• Despite claims to the contrary, KiDS data are internally consistent.• Despite claims to the contrary, DES might not be consistent with Planck.
→ The next rounds of analyses by KiDS, DES, and HSC will ramp up precision and accuracy – then we will know if this is fluke or feature.
KiDS measurements: http://kids.strw.leidenuniv.nl/sciencedata.php