E. Bertin DES Munich meeting 05/2010 1

Deblending

Deblending in DESDMDeblending in DESDM

E.Bertin (IAP)E.Bertin (IAP)

E. Bertin DES Munich meeting 05/2010 2

Deblending

Deblending

• Detecting sub-components• Recovering objects from the

sub-components• Forthcoming developments

E. Bertin DES Munich meeting 05/2010 3

Deblending

How sources are detected in SExtractor

• 4 steps:– Sky background

modeling and subtraction

– Image filtering at the PSF scale (matched filter)

– Thresholding and image segmentation

– Merging and/or splitting of detections

E. Bertin DES Munich meeting 05/2010 4

Deblending

Detecting sub-components

• SExtractor (or COSMOS): Multithresholding– Removal of noise

peaks based on local constrast ratio

• Photo (or DAOPhot): peak detection

• IMCat: multiscale peak detection

• SExtractor _PSF parameters: multiple PSF fitting with proximity constraints

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E. Bertin DES Munich meeting 05/2010 5

Deblending

IMCAT empircal multiscale approach

Kaiser et al. 1995

E. Bertin DES Munich meeting 05/2010 6

Deblending

Wavelet analysis

Starck et al. 2000Starck et al. 2000

• Extend the benefit of filtering from point-sources to very extended objects• Wavelet analysis: a data cube w( x,a) is obtained by correlating the image with the basis functions

is localized, isotropic, and has zero mean.•The last difficult (yet unsolved) step is to connect the detections done at each scale to reconstruct the final object (Bijaoui & Rué 1995).• pyramidal median transform is an alternative to wavelet decomposition (Starck et al. 1995)

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E. Bertin DES Munich meeting 05/2010 7

Deblending

Recovering objects

• Sextractor: 1 pixel « belongs » to one object only– Pixels lying close to

boundaries are reassociated to an object on a statistical basis (dithering)

• Photo: flux fractions reassociated based on fits of « symmetrized » templates

Lupton 2005

E. Bertin DES Munich meeting 05/2010 8

Deblending

Image segmentation in SExtractor

E. Bertin DES Munich meeting 05/2010 9

Deblending

Suggested improvements

• Drop the assumption: 1 source per pixel– Still looking for a way to do that in the cleanest way– Allow to do multiple source fits?

• Try to deblend source blends that show no saddle in their profiles?

• Multichannel deblending?• Metrics to measure deblending performance?

– Cluster simulation in SkyMaker