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

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-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,

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5 is enough

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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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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

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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 /

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(e.g. Hu 1999, Ma, Hu, Huterer 2006, Jain et al 2007,Amara & Refregier 2007 ....)

Relatively flat

Impact of increasing z

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Number of spectra

103 105 107

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Color tomography

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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

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pec

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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

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