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

3. Tyson et al 2002

4. Cosmic shear tomography  

5.  

6. Sensitivity in each z bin

7. Dark Energy Task Force report astro-ph/0609591 SKA calculations based on predictionso by Abdalla & Rawlings 2005

8. Cosmic Shear: Potential systematics Shear measurement Photometric redshifts Intrinsic alignments Accuracy of predictions Measurement Astrophysical Theoretical

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

10. Typical star Used for finding Convolution kernel Typical galaxy used for cosmic shear analysis

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

14. Cosmic Lensing Real data: g i ~0.03 g i ~0.2

15. Atmosphere and Telescope Convolution with kernel Real data: Kernel size ~ Galaxy size

16. Pixelisation Sum light in each square Real data: Pixel size ~ Kernel size /2

17. Noise Mostly Poisson. Some Gaussian and bad pixels. Uncertainty on total light ~ 5 per cent

19. 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

20. STEP1 Results Heymans et al 2005 -20%20% Accuracy on g The future requires 0.0003 → Existing results are reliable -0.2 0.2

21. STEP results - Dirty laundry Accuracy on g 0 Average -0.0010 ~ noise level of image -0.005 Low noiseHigh noise Require 0.0003

22. www.great08challenge.info

24. GREAT08 Data One galaxy per image Kernel is given One shear per set Noise is Poisson ~10 000 images divided into ~10 sets ~100 000 000 images Divided into ~1000 sets

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

26. GREAT08 Summary 100 million images 1 galaxy per image De-noise, de-convolve, average → shear g i ~ 0.03 to accuracy 0.0003 → Q~1000 → Win!

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

28. Sensitivity in each z bin

29. How many redshift bins to use? Ma, Hu & Huterer 5 is enough Modified from

31. 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)

32. Cosmic shear tomography zz

33. A case study: the DUNE satellite Photometric redshift biases: Catastrophic outliers Uninformative region Biases Abdalla et al. astro-ph:0705.1437 Slide from Filipe Abdalla

34. 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

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

36. Bernstein & Ma 2008 Number of spectra 10 3 10 5 10 7 Dark energy degradation (w a )

37. Color tomography Jain, Connolly & Takada

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

40. Cosmic shear (2 point function)

41. Gravitationally sheared Gravitationally sheared Lensing by dark matter causes galaxies to appear aligned Cosmic shear Face-on view

42. 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

43. Tidal stretching causes galaxies to align Adds to cosmic shear signal Intrinsically Aligned (I) Intrinsically Aligned (I) Intrinsic alignments (II) Face-on view

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

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

46. Cosmic shear two point tomography

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

48. 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)

51. Removal of intrinsic alignments using the redshift dependence

54. 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)

55. GI nulling (Joachimi & Schneider 2008)

58. 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 2007....) Relatively flat

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

60. Photoz error σz / (1+z) FoM / FoM(specz) A factor of ~3 better photozs required! 0.8 0.02 (1+z)0.08 (1+z)

61. 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_apr08 http://docs.google.com/View?docid=dcrd4nqb_34d9st35cs

62. 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

63. cosmocoffee.info