image deconvolution of xmm-newton data

Image Deconvolution of Image Deconvolution of XMM-Newton DataXMM-Newton Data

Tao Song, Steve SembayTao Song, Steve Sembay

Dept. Physics & AstronomyDept. Physics & AstronomyUniversity of LeicesterUniversity of Leicester

OverviewOverview

• IntroductionIntroduction

• Richardson-Lucy AlgorithmRichardson-Lucy Algorithm

• IDL programIDL program

• ExamplesExamples

• Future worksFuture works

Vela PWNeChandra

EPIC-MOSDeconvolved

IntroductionIntroduction

• Observed images are usually degraded, i.e. the Observed images are usually degraded, i.e. the shape of a target will be distorted by the PSF.shape of a target will be distorted by the PSF.

• Image deconvolution is to recover the original Image deconvolution is to recover the original scene from the observed degraded data.scene from the observed degraded data.

• Two types of algorithms: empirical (e.g. CLEAN) Two types of algorithms: empirical (e.g. CLEAN) and theoretical (e.g. Richardson-Lucy)and theoretical (e.g. Richardson-Lucy)

• IDL software was developed to do image IDL software was developed to do image deconvolution on XMM-Newton datadeconvolution on XMM-Newton data

• Modified Richardson-Lucy algorithms and blind Modified Richardson-Lucy algorithms and blind deconvolution algorithm were testeddeconvolution algorithm were tested

Richardson-Lucy Richardson-Lucy DeconvolutionDeconvolution

)1( dxξ|xPxx

ΨξΨ rr1r

where dξξ|xPξΨx rrr

)2(' dxξ|xΨxx

PξP rr1r

)3(

1

1

2

2

n

iri

rii

n

where n is the number of pixels in a image.

IDL program – IDL program – main panelmain panel

IDL program – IDL program – detail paneldetail panel

ExamplesExamples – – P0401240501M1S001MIEVLI0000.FITP0401240501M1S001MIEVLI0000.FIT

PSF Data: PSF Data: PSF_M1_0a_VelaPSR_110208_i3_s1.fitsPSF_M1_0a_VelaPSR_110208_i3_s1.fits

•1.1’’ per pixel1.1’’ per pixel•FLAG == 0FLAG == 0•CCDNR == 1CCDNR == 1•PATTERN == 0PATTERN == 0

•50 Iterations50 Iterations22 = 1.80 = 1.80


Peak: 52277 (Y=244)FWHM: 3.14

(242.37~245.51)

Peak: 51398 (X=268)FWHM: 3.29

(266.70~269.99)

Peak: 14647 (Y=244)FWHM: 7.45

(240.28~247.73)

Peak: 14191 (X=269)FWHM: 8.32

(264.58~272.91)

ExamplesExamples – – P011108001M1S001MIEVLI000.FITP011108001M1S001MIEVLI000.FIT•1.1’’ per pixel1.1’’ per pixel•FLAG == 0FLAG == 0•CCDNR == 1CCDNR == 1•PATTERN == 0PATTERN == 0

•50 Iterations50 Iterations22 = 1.33 = 1.33

PSF Data: PSF Data: PSF_M1_0a_VelaPSR_110208_i3_s1.fitsPSF_M1_0a_VelaPSR_110208_i3_s1.fits


Peak: 35000 (Y=235)FWHM: 4.19

(232.96~237.15)

Peak: 30031 (X=257)FWHM: 4.39

(254.35~258.74)

Peak: 13385 (Y=235)FWHM: 14.18

(227.38~241.56)

Peak: 11595 (X=257)FWHM: 13.79

(249.44~263.23)

ExamplesExamples – blind deconvolution – blind deconvolution

22 = 1.20 = 1.20vs.vs.

22 = 1.33 = 1.33

22 = 1.24 = 1.24vs.vs.

22 = 1.80 = 1.80

ExamplesExamples – blind deconvolution – blind deconvolutionOutput strongly depends on the initial inputs, i.e. observed imageOutput strongly depends on the initial inputs, i.e. observed image and PSF and PSF


A hint ?A hint ?(which one is (which one is more proper ?)more proper ?)


22 = 1.77 vs. = 1.77 vs. 22 = 1.80 = 1.80

Peak: 56119 (Y=244)FWHM: 3.07

(242.38~245.45)

Peak: 54883 (X=268)FWHM: 3.19

(266.71~269.90)

Peak: 52277 (Y=244)FWHM: 3.14

(242.37~245.51)

Peak: 51398 (X=268)FWHM: 3.29

(266.70~269.99)


22 = 1.33 vs. = 1.33 vs. 22 = 1.33 = 1.33

Peak: 36227 (Y=235)FWHM: 4.17

(232.94~237.10)

Peak: 31070 (X=257)FWHM: 4.38

(254.32~258.70)

Peak: 35000 (Y=235)FWHM: 4.19

(232.96~237.15)

Peak: 30031 (X=257)FWHM: 4.39

(254.35~258.74)

Future worksFuture works

• Apply image deconvolution on more Apply image deconvolution on more XMM-Newton DataXMM-Newton Data

• More tests on different PSFs (based on More tests on different PSFs (based on the outputs of blind deconvolution)the outputs of blind deconvolution)

• Modifications on original Richardson-Modifications on original Richardson-Lucy algorithmLucy algorithm

• More functions in the IDL programMore functions in the IDL program

image deconvolution of xmm-newton data

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