Cosmic Shear: Potential and Prospects Shear measurement Photometric redshifts Intrinsic alignments Sarah Bridle, UCL (London)

Tyson et al 2002

Cosmic shear tomography  

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Sensitivity in each z bin

Dark Energy Task Force report astro-ph/ 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 star Used for finding Convolution kernel Typical galaxy used for cosmic shear analysis

Gravitational Lensing Galaxies seen through dark matter distribution analogous to Streetlamps seen through your bathroom window

Cosmic Lensing Real data: g i ~0.03 g i ~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 Heymans et al %20% Accuracy on g The future requires → Existing results are reliable

STEP results - Dirty laundry Accuracy on g 0 Average ~ noise level of image Low noiseHigh noise Require

GREAT08 Data One galaxy per image Kernel is given One shear per set Noise is Poisson ~ images divided into ~10 sets ~ images Divided into ~1000 sets

GREAT08 Active Leaderboard You submit g 1, g 2 for each set of images

GREAT08 Summary 100 million images 1 galaxy per image De-noise, de-convolve, average → shear g i ~ 0.03 to accuracy → 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 & Huterer 5 is enough Modified from

Training Set Methods Determine functional relation Examples Neural Network (Firth, Lahav & Somerville 2003; Collister & Lahav 2004) Polynomial Nearest Neighbors (Cunha et al. in prep. 2005) Template Fitting methods 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) Slide from Filipe Abdalla Also: cross correlations (Newman, Zhan, Schneider, Bernstein)

Cosmic shear tomography zz

A case study: the DUNE satellite Photometric redshift biases: Catastrophic outliers Uninformative region Biases Abdalla et al. astro-ph: Slide from Filipe Abdalla

Problems with photozs Smearing in the z direction –Photoz uncertainty  z –Shape of P(z phot |z spec ) Uncertainty in n(z) –Uncertainty in  z –Uncertainty in z bias Get more filters Get spectra See Ma, Hu, Huterer 2005; Huterer, Takada, Bernstein, Jain 2003; Bernstein & Ma 2008

Photoz error σ z / (1+z) FoM / FoM(specz) (e.g. Hu 1999, Ma, Hu, Huterer 2006, Jain et al 2007, Amara & Refregier ) Relatively flat Impact of increasing  z

Bernstein & Ma 2008 Number of spectra Dark energy degradation (w a )

Color tomography Jain, Connolly & Takada

Cosmic Shear: Potential systematics Shear measurement Photometric redshifts Intrinsic alignments Accuracy of predictions

Cosmic shear (2 point function)

Gravitationally sheared Gravitationally sheared Lensing by dark matter causes galaxies to appear aligned Cosmic shear Face-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 align Adds to cosmic shear signal Intrinsically Aligned (I) Intrinsically Aligned (I) Intrinsic alignments (II) Face-on view

Intrinsic-shear correlation (GI) Hirata & Seljak 2004 See also Heymans et al 2006, Mandelbaum et al 2006, Hirata et al 2007

Galaxies point in opposite directions Partially cancels cosmic shear signal Gravitationally sheared (G) Intrinsically aligned (I) Intrinsic-shear correlation (GI) Face-on view

Cosmic shear two point tomography

Cosmic Shear Intrinsic Alignments (IA) Normalised to Super-COSMOS Heymans et al 2004

If consider only w then IA bias on w is ~10% If marginalise 6 cosmological parameters then IA bias on w is ~100% (+/- 1 !) Intrinsic Alignments (IA)

Removal of intrinsic alignments using 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 Alignments FoM / FoM(specz) (e.g. Hu 1999, Ma, Hu, Huterer 2006, Jain et al 2007, Amara & Refregier ) Relatively flat

Photoz error σz / (1+z) Reasonable model? (14 IA pars) Very flexible (100 IA pars) FoM / FoM(specz)

Photoz error σz / (1+z) FoM / FoM(specz) A factor of ~3 better photozs required! (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:

Conclusions Shear measurement –A pure statistics problem –GREAT08 Photometric redshifts –Cosmic shear alone places light requirements on  z –Need ~10 5 spectra –PHAT Intrinsic alignments –3 times tighter requirements on photoz  z –Currently investigating additional measurements

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