1. Image Deconvolution of XMM-Newton Data
Tao Song, Steve Sembay
Dept. Physics & Astronomy
University of Leicester

2. Overview Introduction Richardson-Lucy Algorithm IDL program Examples
Future works

3. Vela PWNe
Chandra
EPIC-MOS
Deconvolved

4. Introduction
Observed images are usually degraded, i.e. the shape of a target will be distorted by the PSF.
Image deconvolution is to recover the original scene from the observed degraded data.
Two types of algorithms: empirical (e.g. CLEAN) and theoretical (e.g. Richardson-Lucy)
IDL software was developed to do image deconvolution on XMM-Newton data
Modified Richardson-Lucy algorithms and blind deconvolution algorithm were tested

5. Richardson-Lucy Deconvolution
where
where n is the number of pixels in a image.

6. IDL program – main panel

7. IDL program – detail panel

8. Examples – P0401240501M1S001MIEVLI0000.FIT
1.1’’ per pixel
FLAG == 0
CCDNR == 1
PATTERN == 0
50 Iterations
2 = 1.80
PSF Data: PSF_M1_0a_VelaPSR_110208_i3_s1.fits

9. Examples – P0401240501M1S001MIEVLI0000.FIT
Peak: (Y=244)
FWHM: 3.14 (242.37~245.51)
Peak: (X=268)
FWHM: 3.29 (266.70~269.99)
Peak: (Y=244)
FWHM: 7.45 (240.28~247.73)
Peak: (X=269)
FWHM: 8.32 (264.58~272.91)

10. Examples – P011108001M1S001MIEVLI000.FIT
1.1’’ per pixel
FLAG == 0
CCDNR == 1
PATTERN == 0
50 Iterations
2 = 1.33
PSF Data: PSF_M1_0a_VelaPSR_110208_i3_s1.fits

11. Examples – P011108001M1S001MIEVLI000.FIT
Peak: (Y=235)
FWHM: 4.19 (232.96~237.15)
Peak: (X=257)
FWHM: 4.39 (254.35~258.74)
Peak: (Y=235)
FWHM: (227.38~241.56)
Peak: (X=257)
FWHM: (249.44~263.23)

12. Examples – blind deconvolution
2 = 1.24
vs.
2 = 1.80
2 = 1.20
vs.
2 = 1.33

13. Examples – blind deconvolution
Output strongly depends on the initial inputs, i.e. observed image and PSF

14. Examples – blind deconvolution
A hint ?
(which one is
more proper ?)

15. Examples – blind deconvolution
2 = vs 2 = 1.80
Peak: (Y=244)
FWHM: 3.07 (242.38~245.45)
Peak: (X=268)
FWHM: 3.19 (266.71~269.90)
Peak: (Y=244)
FWHM: 3.14 (242.37~245.51)
Peak: (X=268)
FWHM: 3.29 (266.70~269.99)

16. Examples – blind deconvolution
2 = vs 2 = 1.33
Peak: (Y=235)
FWHM: 4.17 (232.94~237.10)
Peak: (X=257)
FWHM: 4.38 (254.32~258.70)
Peak: (Y=235)
FWHM: 4.19 (232.96~237.15)
Peak: (X=257)
FWHM: 4.39 (254.35~258.74)

17. Future works Apply image deconvolution on more XMM-Newton Data
More tests on different PSFs (based on the outputs of blind deconvolution)
Modifications on original Richardson-Lucy algorithm
More functions in the IDL program