cosmic shear: potential and prospects shear measurement photometric redshifts intrinsic alignments...
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Cosmic Shear: Potential and Prospects
• Shear measurement
• Photometric redshifts
• Intrinsic alignments
Sarah Bridle, UCL (London)
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Cosmic shear tomography
Cosmic shear tomography
Sensitivity in each z bin
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SKA calculations based on predictionso by Abdalla & Rawlings 2005
Cosmic Shear: Potential systematics
Shear measurement
Photometric redshifts
Intrinsic alignments
Accuracy of predictions
Measurement
Astrophysical
Theoretical
Cosmic Shear: Potential systematics
Shear measurement
Photometric redshifts
Intrinsic alignments
Accuracy of predictions
Typical starUsed for finding Convolution kernel
Typical galaxyused for cosmicshear analysis
Gravitational Lensing
Galaxies seen through dark matter distribution analogous to
Streetlamps seen through your bathroom window
Cosmic Lensing
Real data:gi~0.03
gi~0.2
Atmosphere and Telescope
Convolution with kernel
Real data: Kernel size ~ Galaxy size
Pixelisation
Sum light in each square
Real data: Pixel size ~ Kernel size /2
Noise
Mostly Poisson. Some Gaussian and bad pixels.Uncertainty on total light ~ 5 per cent
Shear TEsting Programme (STEP)
• Started July 2004
• Is the shear estimation problem solved or not?
• Series of international blind competitions– Start with simple simulated data (STEP1)– Make simulations increasingly realistic– Real data
• Current status:– STEP 1: simplistic galaxy shapes (Heymans et al 2005)– STEP 2: more realistic galaxies (Massey et al 2006)– STEP 3: difficult (space telescope) kernel (2007)– STEP 4: back to basics See Konrad’s Edinburgh DUEL talk
STEP1 Results
Hey
man
s et
al 2
005
-20% 20%Accuracy on g
The future requires 0.0003
→ Existing results
are reliable
-0.2 0.2
STEP results - Dirty laundry
Accuracyon g
0Average -0.0010
~ noise level of image
-0.005
Low noise High noise
Require 0.0003
www.great08challenge.info
www.great08challenge.info
GREAT08 Data
One galaxy per imageKernel is givenOne shear per setNoise is Poisson
~10 000 imagesdivided into ~10 sets
~100 000 000 images
Divided into ~1000 sets
GREAT08 Active Leaderboard
You submit g1, g2 for each set of images
GREAT08 Summary
• 100 million images
• 1 galaxy per image
• De-noise, de-convolve, average → shear
• gi ~ 0.03 to accuracy 0.0003 → Q~1000 → Win!
Cosmic Shear: Potential systematics
Shear measurement
Photometric redshifts
Intrinsic alignments
Accuracy of predictions
Sensitivity in each z bin
How many redshift bins to use?
Ma,
Hu
& H
ute
rer
5 is enough
Mo
dif
ied
fro
m
Training Set Methods
• Determine functional relation
zphot zphot (m,c)
• Examples
Neural Network(Firth, Lahav & Somerville 2003; Collister & Lahav 2004)
Polynomial Nearest Neighbors(Cunha et al. in prep. 2005)
Template Template Fitting Fitting methodsmethods
• Use a set of standard SED’s - templates (CWW80, etc.)
• Calculate fluxes in filters of redshifted templates.
• Match object’s fluxes (2 minimization)
• Outputs type and redshift
• Bayesian Photo-z
Hyper-z (Bolzonella et al. 2000) BPZ (Benitez 2000)
Polynomial(Connolly et al. 1995)
Nearest Neighbors(Csabai et al. 2003)
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alla
Also: cross correlations (Newman, Zhan, Schneider, Bernstein)
Cosmic shear tomographyz
• A case study: the DUNE satellite
Photometric redshift biases:
Catastrophicoutliers
Uninformativeregion
Biases
Abdalla et al. astro-ph:0705.1437
Sli
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m F
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Problems with photozs
• Smearing in the z direction– Photoz uncertainty z
– Shape of P(zphot|zspec)
• Uncertainty in n(z)– Uncertainty in z
– Uncertainty in zbias
Get more filters
Get spectra
See Ma, Hu, Huterer 2005; Huterer, Takada, Bernstein, Jain 2003; Bernstein & Ma 2008
Photoz error σz / (1+z)
Fo
M /
Fo
M(s
pec
z)
(e.g. Hu 1999, Ma, Hu, Huterer 2006, Jain et al 2007,Amara & Refregier 2007 ....)
Relatively flat
Impact of increasing z
Ber
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& M
a 20
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Number of spectra
103 105 107
Dar
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y d
egra
dat
ion
(w
a)
Color tomography
Jain
, C
on
no
lly
& T
akad
a
Cosmic Shear: Potential systematics
Shear measurement
Photometric redshifts
Intrinsic alignments
Accuracy of predictions
Cosmic shear (2 point function)
Gravitationallysheared
Gravitationallysheared
Lensing by dark matter causes galaxies to appear aligned
Cosmic shearFace-on view
Intrinsic alignments (II)
Croft & Metzler 2000, Heavens et al 2000, Crittenden et al 2001, Catelan et al 2001, Mackey et al, Brown et al
2002, Jing 2002, Hui & Zhang 2002
Tidal stretching causes galaxies to alignAdds to cosmic shear signal
IntrinsicallyAligned (I)
IntrinsicallyAligned (I)
Intrinsic alignments (II)Face-on view
Intrinsic-shear correlation (GI)
Hirata & Seljak 2004See also Heymans et al 2006, Mandelbaum et al 2006,
Hirata et al 2007
Galaxies point in opposite directionsPartially cancels cosmic shear signal
Gravitationallysheared (G)
Intrinsicallyaligned (I)
Intrinsic-shear correlation (GI)Face-on view
Cosmic shear two point tomography
CosmicShear
IntrinsicAlignments (IA)
Normalised to Super-COSMOSHeymans et al 2004
If consider only wthen IA bias on wis ~10%
If marginalise 6 cosmologicalparametersthen IA bias on w is ~100% (+/- 1 !)
IntrinsicAlignments (IA)
Removal of intrinsic alignmentsusing the redshift dependence
Removal of intrinsic alignmentsusing the redshift dependence
Removal of intrinsic alignmentsusing the redshift dependence
Removal of intrinsic alignments
• Intrinsic – intrinsic (II) – Weight down close pairs (King & Schneider 2002,
Heymans & Heavens 2003, Takada & White 2004)
– Fit parameterized models (King & Schneider 2003)
• Shear – intrinsic (GI)– Redshift weighting (Joachimi & Schneider 2008)
– Fit parameterized models (King 2005, Bernstein DETF)
GI nulling (Joachimi & Schneider 2008)
Photoz error σz / (1+z)
No Intrinsic AlignmentsF
oM
/ F
oM
(sp
ecz)
(e.g. Hu 1999, Ma, Hu, Huterer 2006, Jain et al 2007,Amara & Refregier 2007 ....)
Relatively flat
Photoz error σz / (1+z)
Reasonable model? (14 IA pars)
Very flexible (100 IA pars)
Fo
M /
Fo
M(s
pec
z)
Photoz error σz / (1+z)
Fo
M /
Fo
M(s
pec
z)A factor of ~3 better photozs required!
0.8
0.02 (1+z) 0.08 (1+z)
Future work on intrinsic alignments
• Analytic predictions– Identify physical origin of contributions– Provide fitting functions to compare with data
• n-body and hydro simulations– Compare with analytic predictions– Test effectiveness of removal methods
• Observational constraints– From other statistics and using spectra
For more information see:http://zuserver2.star.ucl.ac.uk/~sarah/ia_ucl_apr08http://docs.google.com/View?docid=dcrd4nqb_34d9st35cs
Conclusions• Shear measurement
– A pure statistics problem– GREAT08
• Photometric redshifts– Cosmic shear alone places light requirements on z
– Need ~105 spectra– PHAT
• Intrinsic alignments– 3 times tighter requirements on photoz z
– Currently investigating additional measurements
cosmocoffee.info