1. Quantifying Dark Energy using Cosmic Shear
Thank introducer. Thank everyone for coming.
Sarah Bridle University College London

2. UCL Astrophysics Group Cosmologists
Ofer Lahav
Ole Host
Cris Sabiu
Tomek Kacprzak
Barney Rowe
Michael Hirsch
Sarah Bridle
Stephanie Jouvel
Fotini Economou
Adam Hawken
Emily Hall
Lisa Voigt
Joe Zuntz
Jason McEwen
Filipe Abdalla
Emma Chapman
Maayane Sougmanac
Laura Wolz
Boris Leistedt
Stephen Feeney
Hiranya Peiris

3. Statistical Cosmology from Surveys
Filipe Abdalla
Cosmology
EoR
Weak lensing
Euclid DES
SKA
Sarah Bridle
Cosmic Shear
Galaxy shapes
DES
Dark energy
Ofer Lahav
Galaxy surveys
MegaZ-LRG 2MRS
Photo-z
HST-MCT
DES Euclid
Hiranya Peiris
Early Universe
Planck
Inflation
Isotropy
Non-Gaussianity
1,11 1
1,6,8
1,4,5
1,2,9,10,
12, 13
1,4
1,3,6
1,3,7
1,3
GR tests

4. Quantifying Dark Energy Using Cosmic Shear
Introduction to Cosmic Shear
Potential limitations
Shear measurement
Intrinsic alignments
Dark Energy Survey

5. Quantifying Dark Energy Using Cosmic Shear
Introduction to Cosmic Shear
Potential limitations
Shear measurement
Intrinsic alignments
Dark Energy Survey

6. Concordance Model 75% Dark Energy 5% Baryonic Matter
20% Cold Dark Matter

7. Why is the Universe Accelerating?
Einstein’s cosmological constant
A new fluid called Dark Energy
Equation of state w = p/
General Relativity is wrong
We live in an underdense region

8. The Future
JDEM

9. Comparison of different methods
Galaxy
clustering
Supernovae
Gravitational
shear
Quality of dark energy constraint
Example for optical ground-based surveys
Dark Energy Task Force report astro-ph/
Gravitational shear has the greatest potential
Big uncertainty largely due to shear measurement techniques
Maybe dark energy is the wrong model...

10. Seeing the Invisible: Is there something in between us and the wall and tree?

11. Just one Equation from General Relativity

15. Simulated Dark Matter Map

16. Shear Map

18. Results from the HST COSMOS Survey

19. The Largest ever Survey with HST
Credit: NASA, ESA and R. Massey (California Institute of Technology)

20. Credit: NASA, ESA and R. Massey (California Institute of Technology)

21. The Visible The Invisible
Credit: NASA, ESA and R. Massey (California Institute of Technology)
Credit: NASA, ESA and R. Massey (California Institute of Technology)

22. In 3 Dimensions
Pictures + videos from

24. Quantifying Dark Energy Using Cosmic Shear
Introduction to Cosmic Shear
Potential limitations
Shear measurement
Intrinsic alignments
Dark Energy Survey

25. Comparison of different methods
Galaxy
clustering
Supernovae
Gravitational
shear
Quality of dark energy constraint
Example for optical ground-based surveys
Dark Energy Task Force report astro-ph/
Gravitational shear has the greatest potential
Big uncertainty largely due to shear measurement techniques
Maybe dark energy is the wrong model...

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

27. Quantifying Dark Energy Using Cosmic Shear
Introduction to Cosmic Shear
Potential limitations
Shear measurement
Intrinsic alignments
Dark Energy Survey

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

29. Cosmic Shear
gi~0.2
Real data:
gi~0.03

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

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

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

35. GREAT08 Data

36. GREAT08 Results in Detail
Bridle et al 2010

37. GREAT10 led by Tom Kitching

38. GREAT3 and beyond Galaxies with more complicated shapes
Kernel is more realistic
Many overlapping objects in each image
Correlated noise
Poisson + Gaussian noise + bad pixels
Cosmic rays, satellite tracks, saturated stars
Multiple exposures
Pixels not exactly square or on a grid
Wavelength dependent kernel
GREAT3 led by Barney Rowe and Rachel Mandelbaum

39. m3shape Shear Measurement Code
Tomek Kacprzak
Barney Rowe
Michael Hirsch
Sarah Bridle
Lisa Voigt
Joe Zuntz
Real-space forward modeller.
Optimizer, MCMC, image & likelihood toolkits
Multi-model: Bulge/Disc, PSF, weights, high-res
Easily portable C

40. Quantifying Dark Energy Using Cosmic Shear
Introduction to Cosmic Shear
Potential limitations
Shear measurement
Intrinsic alignments
Dark Energy Survey

41. Cosmic shear (2 point function)

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

43. 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

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

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

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

47. Cosmic shear tomography 

48. Cosmic shear tomography 

49. Effect on cosmic shear of changing w by 1% Cosmic Shear Intrinsic
Alignments (IA)
Don’t explain details of whether GI, II
Normalised to Super-COSMOS
Heymans et al 2004

50. Effect on cosmic shear of changing w by 1% Intrinsic Alignments (IA)
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)
Move along degeneracies -> 100%
We have to consider intrinsic alignments, and remove them carefully

51. Removal of intrinsic alignments using the redshift dependence
Laszlo, Kirk, Bean, Bridle 2011

52. Use of shear-position correlations
Shear-shear correlations
- Measure mostly dark matter
Shear-position correlations
- Measure mostly intrinsic alignments
Position-position correlations
- Traditional galaxy survey observable
Joachimi & Bridle 2010

53. Use wrong
Intrinsic
Alignment
model
Kirk, Rassat, Host, Bridle, Voigt 2011
Marginalise
100 free
Parameters
in Intrinsic
Alignment
model

54. Quantifying Dark Energy Using Cosmic Shear
Introduction to Cosmic Shear
Potential limitations
Shear measurement
Intrinsic alignments
Dark Energy Survey

55. The Future
JDEM

56. The Dark Energy Survey Future prospects
Cosmic microwave background radiation
Distribution of dark matter at early times
Distribution of galaxies
Some clues to distribution of matter
Galaxy velocities
Galaxies fall towards dark matter clumps
Gravitational lensing

57. The Dark Energy Survey Blanco 4-meter at CTIO
Survey project using 4 complementary techniques:
I. Cluster Counts
II. Weak Lensing
III. Large-scale Structure
IV. Supernovae
• Two multiband surveys:
5000 deg2 grizY to 24th mag
30 deg2 repeat (SNe)
• Build new 3 deg2 FOV camera
and Data management system
Survey (525 nights)
Facility instrument for Blanco

58. The DES Collaboration Fermilab
University of Illinois at Urbana-Champaign/NCSA
University of Chicago
Lawrence Berkeley National Lab
NOAO/CTIO
DES Spain Consortium
DES United Kingdom Consortium
University of Michigan
Ohio State University
University of Pennsylvania
DES Brazil Consortium
Argonne National Laboratory
SLAC-Stanford-Santa Cruz Consortium
Universitats-Sternwarte Munchen
Texas A&M University
plus Associate members at:
Brookhaven National Lab, U. North Dakota, Paris, Taiwan
Over 120 members
plus students & postdocs
Funding: DOE, NSF; UK: STFC, SRIF; Spain Ministry of Science, Brazil: FINEP, Ministry of Science, FAPERJ; Germany: Excellence Cluster; collaborating institutions
5858

59. The DES Collaboration (Oct 2010)

60. CCD Readout Filters Shutter Hexapod Corrector Lenses
Mechanical Interface of DECam Project to the Blanco
CCD
Readout
Focal plane (detector)
Filters
Shutter
Hexapod
Corrector
Lenses

61. Lenses to leave UCL shortly

62. DECam CCDs
62 2kx4k fully depleted CCDs: 520 Megapixels, 250 micron thick, 15 micron (0.27”) pixel size
12 2kx2k guide and focus chips
Excellent red sensitivity
Roughly twice the number of science-grade CCDs packaged and ready to install
Developed by LBNL

63. DECam Image Simulations Series of Data Challenges to test Data Management System
Populate N-body sims w/ galaxies drawn from SDSS+evolution+shapes

64. DECam Image Simulations Series of Data Challenges to test Data Management System
the Science working groups analysed 200 square degrees of imaging data (DC5), currently working on the next round, with even more realistic data
Note bright star artifacts, cosmic rays, cross talk, glowing edges, flatfield (“grind marks”, tape bumps), bad columns, 2 amplifiers/CCD.

65. DES Optics Requirements
Antonik, Bacon, Bridle, Doel et al in prep

66. Summary Cosmic shear the greatest potential of all for DE
Need better shape measurement methods
Understanding of intrinsic alignments is vital
Dark Energy Survey is nearing installation