1 / 17

Image Deconvolution of XMM-Newton Data

Image Deconvolution of XMM-Newton Data. Tao Song, Steve Sembay Dept. Physics & Astronomy University of Leicester. Overview. Introduction Richardson-Lucy Algorithm IDL program Examples Future works. Vela PWNe. Chandra. EPIC-MOS. Deconvolved. Introduction.

stacy-potts
Download Presentation

Image Deconvolution of XMM-Newton Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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. Peak: 52277 (Y=244) FWHM: 3.14 (242.37~245.51) Peak: 14647 (Y=244) FWHM: 7.45 (240.28~247.73) Peak: 51398 (X=268) FWHM: 3.29 (266.70~269.99) Peak: 14191 (X=269) FWHM: 8.32 (264.58~272.91) Examples–P0401240501M1S001MIEVLI0000.FIT

  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. Peak: 35000 (Y=235) FWHM: 4.19 (232.96~237.15) Peak: 13385 (Y=235) FWHM: 14.18 (227.38~241.56) Peak: 30031 (X=257) FWHM: 4.39 (254.35~258.74) Peak: 11595 (X=257) FWHM: 13.79 (249.44~263.23) Examples–P011108001M1S001MIEVLI000.FIT

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

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

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

  15. Peak: 56119 (Y=244) FWHM: 3.07 (242.38~245.45) Peak: 52277 (Y=244) FWHM: 3.14 (242.37~245.51) Peak: 54883 (X=268) FWHM: 3.19 (266.71~269.90) Peak: 51398 (X=268) FWHM: 3.29 (266.70~269.99) Examples– blind deconvolution 2 = 1.77 vs. 2 = 1.80

  16. Peak: 36227 (Y=235) FWHM: 4.17 (232.94~237.10) Peak: 35000 (Y=235) FWHM: 4.19 (232.96~237.15) Peak: 31070 (X=257) FWHM: 4.38 (254.32~258.70) Peak: 30031 (X=257) FWHM: 4.39 (254.35~258.74) Examples– blind deconvolution 2 = 1.33 vs. 2 = 1.33

  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

More Related