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

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

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

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

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

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

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

The Future JDEM

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/0609591 Gravitational shear has the greatest potential Big uncertainty largely due to shear measurement techniques Maybe dark energy is the wrong model...

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

Just one Equation from General Relativity

Simulated Dark Matter Map

Shear Map

Results from the HST COSMOS Survey

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

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

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

In 3 Dimensions Pictures + videos from http://www.spacetelescope.org/news/html/heic0701.html

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

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/0609591 Gravitational shear has the greatest potential Big uncertainty largely due to shear measurement techniques Maybe dark energy is the wrong model...

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

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

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

Cosmic Shear gi~0.2 Real data: gi~0.03

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

GREAT08 Data

GREAT08 Results in Detail Bridle et al 2010

GREAT10 led by Tom Kitching

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

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

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

Cosmic shear (2 point function)

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

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

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

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

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

Cosmic shear tomography  

Cosmic shear tomography  

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

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

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

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

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

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

The Future JDEM

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

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 2012-2017 (525 nights) Facility instrument for Blanco

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

The DES Collaboration (Oct 2010)

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

Lenses to leave UCL shortly

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

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

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.

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

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