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Quantifying Dark Energy with Cosmic Shear. Sarah Bridle, UCL. Dark energy observables Cosmic shear Euclid. Quantifying Dark Energy with Cosmic Shear. Sarah Bridle, UCL. Dark energy observables Cosmic shear Euclid. Concordance Model. 70% Dark Energy. 5% Baryonic Matter.

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Quantifying Dark Energy with Cosmic Shear


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    1. Quantifying Dark Energy with Cosmic Shear Sarah Bridle, UCL • Dark energy observables • Cosmic shear • Euclid

    2. Quantifying Dark Energy with Cosmic Shear Sarah Bridle, UCL • Dark energy observables • Cosmic shear • Euclid

    3. Concordance Model 70% Dark Energy 5% Baryonic Matter 25% Cold Dark Matter

    4. Evidence for dark energy • Supernova observations: • accelerating universe (WL>0)

    5. Fainter Perlmutter et al.1998 Further away

    6. Fainter Accelerating m =1, no DE Decelerating Perlmutter et al.1998 Further away

    7. Dark energy density Constraints from Supernovae Barris et al 2004 Dark matter density

    8. Evidence for dark energy • Supernova observations: • accelerating universe (WL>0) or • Cosmic Microwave Background: • Universe is close to flat (Wm+WL=1) plus

    9. WMAP team WMAP team

    10. Closed Open eg. m=0.3, no DE WMAP team Flat

    11. Closed Flat WMAP team Open

    12. Evidence for dark energy • Supernova observations: • accelerating universe (WL>0) or • Cosmic Microwave Background: • Universe is close to flat (Wm+WL=1) plus • Eg. Hubble constant measurements • H0>~50

    13. WMAP team

    14. WMAP team

    15. The nature of dark energy • Cosmological constant? or • New type of material? • Most generally, describe by pressure: density ratio w = p / r

    16. Observational perspective • Try to measure • amount, WDE • nature of dark energy, w • NB. w could be a function of time w(z)

    17. Kowalski et al 2008

    18. Probes of Dark Energy Cosmic Shear Evolution of dark matter perturbations Angular diameter distance Growth rate of structure Baryon Wiggles Standard ruler Angular diameter distance Supernovae Standard candle Luminosity distance Cluster counts Evolution of dark matter perturbations Angular diameter distance Growth rate of structure CMB Snapshot at ~400,000 yr, viewed from z=0 Angular diameter distance to z~1000 Growth rate of structure (from ISW)

    19. Surveys to measure Dark Energy 2020 2005 2010 KIDS Imaging CFHTLS DES LSST Euclid SKA SDSS VISTA JDEM SUBARU Pan-STARRS Euclid Wiggles DES SKA WiggleZ SDSS ATLAS WFMOS FMOS VISTA Supernovae CSP ESSENCE DES LSST CFHTLS JDEM Pan-STARRS Xeus Clusters AMI APEX SPT DES XCS SZA AMIBA ACT JDEM CMB WMAP3 WMAP5 Planck 2005 2010 2020

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

    21. Quantifying Dark Energy with Cosmic Shear Sarah Bridle, UCL • Dark energy observables • Cosmic shear • Euclid

    22. Cosmic Shear Tyson et al 2002

    23. Pictures + videos from http://www.spacetelescope.org/news/html/heic0701.html

    24. Pictures + videos from http://www.spacetelescope.org/news/html/heic0701.html

    25. Dark matter distribution: observed in COSMOS survey Pictures + videos from http://www.spacetelescope.org/news/html/heic0701.html

    26. Cosmic shear tomography  

    27. Cosmic shear tomography  

    28. Sensitivity at different redshifts

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

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

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

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

    33. Cosmic Lensing gi~0.2 Real data: gi~0.03

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

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

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

    37. www.great08challenge.info www.great08challenge.info

    38. GREAT08 Data 150 000 images divided into 15 sets One galaxy per image Kernel is given One shear per set Noise is Poisson 27 000 000 images Divided into 2700 sets

    39. GREAT08 Data

    40. GREAT08 Active Leaderboard You submit g1, g2 for each set of images

    41. www.great08challenge.info

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

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