Presentation is loading. Please wait.

Presentation is loading. Please wait.

Tom Kitching Tom Kitching.

Similar presentations


Presentation on theme: "Tom Kitching Tom Kitching."— Presentation transcript:

1 Tom Kitching Tom Kitching

2 Coordination Team: THOMAS KITCHING, ADAM AMARA, SARAH BRIDLE, MANDEEP GILL, CATHERINE HEYMANS, RICHARD MASSEY, BARNABY ROWE, LISA VOIGT Advisory Team: SREE BALAN, GARY BERNSTEIN, MATTHIAS BETHGE, FREDERIC COURBIN, MARC GENTILE, STEFAN HARMELING, ALAN HEAVENS, MICHAEL HIRSCH, RESHAD HOSSEINI, DONNACHA KIRK, KONRAD KUIJKEN, RACHEL MANDELBAUM, BABACK MOGHADDAM, GULDARIYA NURBAEVA, STEPHANE PAULIN-HENRIKSSON, ANAIS RASSAT, JASON RHODES, BERNHARD SCHOLKOPF, TIM SCHRABBACK, JOHN SHAWE-TAYLOR, MARINA SHMAKOVA, ANDY TAYLOR, MALIN VELANDER, LUDOVIC VAN WAERBEKE, DUGAN WITHERICK, DAVID WITTMAN

3 The Challenge of Measuring Shapes


4 Typical galaxy used for cosmic shear analysis Typical star
Used for finding Convolution kernel Slide from S. Bridle

5 Cosmic Lensing gi~0.2 Real data: gi~0.03 5/19 Slide from S. Bridle 5

6 Atmosphere and Telescope
Convolution with kernel Real data: Kernel size ~ Galaxy size 6/19 Slide from S. Bridle 6

7 Sum light in each square
Pixelisation Sum light in each square Real data: Pixel size ~ Kernel size /2 7/19 Slide from S. Bridle 7

8 Mostly Poisson. Some Gaussian and bad pixels.
Noise Mostly Poisson. Some Gaussian and bad pixels. Uncertainty on total light ~ 5 per cent 8/19 Slide from S. Bridle 8

9 Need to measure shear to 10-3

10 Current Methods      

11 STEP : Shear Testing Programme
       

12 Heymans et al., 2006; Massey et al., 2007 & Kitching et al., 2008
KSB

13 
       

14

15 Quality Factor Kitching et al., 2008 (form filling functions); Amara & Refregier (2007)

16 7 non-lensing participants
Q~1000 in some regimes

17 GREAT08 : Stacking Procedure is Important
Average Data Individual Object Statistic Ensemble Statistic Average Estimators Winning Methods (Q=1000) Stacked the Data

18 STEP 2006 2010 2008

19   

20 Massey et al. 2008 Fu et al. 2008 Excplitily challenge the winning methods

21

22   
    Bonus Star Challenge: Reconstruct the PSF at non-star positions

23  
Get Ready !         

24 GREAT10 Handbook, Kitching et al., 2010

25

26

27 “Every time the amount of data increases by a factor of ten we should totally rethink the way we analyze it” Jerome Freidman, Data Mining and Statistics, 1997


Download ppt "Tom Kitching Tom Kitching."

Similar presentations


Ads by Google