1 Quantifying Dark Energy using Cosmic ShearThank introducer. Thank everyone for coming. Sarah Bridle University College London
2 UCL Astrophysics Group CosmologistsOfer 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 SurveysFilipe 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 ShearIntroduction to Cosmic Shear Potential limitations Shear measurement Intrinsic alignments Dark Energy Survey
5 Quantifying Dark Energy Using Cosmic ShearIntroduction to Cosmic Shear Potential limitations Shear measurement Intrinsic alignments Dark Energy Survey
6 Concordance Model 75% Dark Energy 5% Baryonic Matter20% 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 methodsGalaxy 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
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15 Simulated Dark Matter Map
16 Shear Map
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18 Results from the HST COSMOS Survey
19 The Largest ever Survey with HSTCredit: NASA, ESA and R. Massey (California Institute of Technology)
20 Credit: NASA, ESA and R. Massey (California Institute of Technology)
21 The Visible The InvisibleCredit: 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
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24 Quantifying Dark Energy Using Cosmic ShearIntroduction to Cosmic Shear Potential limitations Shear measurement Intrinsic alignments Dark Energy Survey
25 Comparison of different methodsGalaxy 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 systematicsShear measurement Measurement Photometric redshifts Astrophysical Intrinsic alignments Accuracy of predictions Theoretical
27 Quantifying Dark Energy Using Cosmic ShearIntroduction 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 TelescopeConvolution with kernel Real data: Kernel size ~ Galaxy size
31 Pixelisation Sum light in each squareReal data: Pixel size ~ Kernel size /2
32 Noise Mostly Poisson. Some Gaussian and bad pixels.Uncertainty on total light ~ 5 per cent
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35 GREAT08 Data
36 GREAT08 Results in DetailBridle et al 2010
37 GREAT10 led by Tom Kitching
38 GREAT3 and beyond Galaxies with more complicated shapesKernel 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 CodeTomek 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 ShearIntroduction 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 alignedCosmic 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 viewIntrinsically 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 viewGravitationally 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 IntrinsicAlignments (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 dependenceLaszlo, Kirk, Bean, Bridle 2011
52 Use of shear-position correlationsShear-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 ShearIntroduction to Cosmic Shear Potential limitations Shear measurement Intrinsic alignments Dark Energy Survey
55 The Future JDEM
56 The Dark Energy Survey Future prospectsCosmic 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 CTIOSurvey 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 FermilabUniversity 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 LensesMechanical 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 SystemPopulate N-body sims w/ galaxies drawn from SDSS+evolution+shapes
64 DECam Image Simulations Series of Data Challenges to test Data Management Systemthe 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 RequirementsAntonik, Bacon, Bridle, Doel et al in prep
66 Summary Cosmic shear the greatest potential of all for DENeed better shape measurement methods Understanding of intrinsic alignments is vital Dark Energy Survey is nearing installation