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Mallorca, Spain, 2009 March 31 – April 1

Update of EPIC pn noise suppression. EPIC Calibration & Operations Meeting. Mallorca, Spain, 2009 March 31 – April 1. number of events. PHA [adu]. “Noise events”: events which are not caused by photons, mainly evident at low Pulse Height Amplitudes (PHAs).

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Mallorca, Spain, 2009 March 31 – April 1

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  1. Update of EPIC pn noise suppression EPIC Calibration & Operations Meeting Mallorca, Spain, 2009 March 31 – April 1

  2. number of events PHA [adu] “Noise events”: events which are not caused by photons, mainly evident at low Pulse Height Amplitudes (PHAs) 50.7 ks closed FF quadrant 0, rev 059

  3. 20-65 adu noise raw_y Previous attempts to suppress the noise events were based on their spatial/spectral characteristics ( epreject )

  4. Previous attempts to suppress the noise events were based on their spatial/spectral characteristics ( epreject ) without noise suppression (epreject) with noise suppression (epreject) Vela SNR, FF, rev 534, 38.8 ks

  5. 20 50 adu However, the spatial/spectral characteristics of noise events turned out to be not sufficiently stable in time to achieve in all cases a satisfactory noise suppression with the concept of a temporally constant noise model.

  6. 20 50 adu However, the spatial/spectral characteristics of noise events turned out to be not sufficiently stable in time to achieve in all cases a satisfactory noise suppression with the concept of a temporally constant noise model.

  7. An improved approach would be to scale the temporally constant noise model individually to each observation, by using information derived from the particular observation. This approach is under investigation, but it is fairly sophisticated, and it is not clear yet whether it will be reliable enough for general usage.

  8. An improved approach would be to scale the temporally constant noise model individually to each observation, by using information derived from the particular observation. This approach is under investigation, but it is fairly sophisticated, and it is not clear yet whether it will be reliable enough for general usage.

  9. An improved approach would be to scale the temporally constant noise model individually to each observation, by using information derived from the particular observation. This approach is under investigation, but it is fairly sophisticated, and it is not clear yet whether it will be reliable enough for general usage. In the process of investigating the temporal properties of noise events, it was found that individual frames are considerably noisier than the average. If these frames are rejected, the signal to noise ratio can be improved at low energies. This method is so straightforward that it can be easily implemented.

  10. Number of frames containing NEVT_FRM events within 20..30 adu FF closed, rev 0462, 23 ks, CCD 1 NEVT_FRM > 7

  11. Number of frames containing NEVT_FRM events within 20..30 adu FF closed, rev 0462, 23 ks, CCD 1 NEVT_FRM > 5

  12. Number of frames containing NEVT_FRM events within 20..30 adu CCD 1 CCD 4 CCD 2 CCD 5 FF closed, rev 0462, 23 ks, CCD 1 – 6 CCD 3 CCD 6

  13. Number of frames containing NEVT_FRM events within 20..30 adu CCD 7 CCD 10 CCD 8 CCD 11 FF closed, rev 0462, 23 ks, CCD 7 - 12 CCD 9 CCD 12

  14. EPIC-pn noise (LW), rev 0790, 61 ks closed, after offset corrections, all events 20 adu 21 adu 22 adu 23 adu 24 adu 25 adu 26 adu 27 adu 28 adu 29 adu 30 adu 31-35 adu 36-40 adu 41-45 adu 46-50 adu

  15. At 30-50 adu, “noise” creates mainly undefined patterns: rev 462 23 ks closed FF no offset corrections all events raw data 30-50 adu

  16. At 30-50 adu, “noise” creates mainly undefined patterns: rev 462 23 ks closed FF no offset corrections non-singles processed data 30-50 adu

  17. At 30-50 adu, “noise” creates mainly undefined patterns: rev 462 23 ks closed FF no offset corrections singles processed data 30-50 adu

  18. LW thick, SNR 1E0102, rev 0803, 30 ks singles, 20 – 30 adu, all frames

  19. SNR 1E 0102, rev 803, LW, thick, 30 ks (reconstructed) number of frames with no event below 31 adu per CCD number of frames with 1 event below 31 adu per CCD number of frames with 2 events below 31 adu per CCD total number of frames number of frames with more than 9 events below 31 adu per CCD

  20. Number of frames containing NEVT_FRM events within 20..30 adu CCD 1 CCD 4 CCD 2 CCD 5 LW thick, SNR 1E0102, rev 0803, 30 ks, CCD 1 – 6 CCD 3 CCD 6

  21. Number of frames containing NEVT_FRM events within 20..30 adu CCD 7 CCD 10 CCD 8 CCD 11 LW thick, SNR 1E0102, rev 0803, 30 ks, CCD 7 – 12 CCD 9 CCD 12

  22. LW thick, SNR 1E0102, rev 0803, 30 ks image image mask image mask applied

  23. LW thick, SNR 1E0102, rev 0803, 30 ks

  24. LW thick, SNR 1E0102, rev 0803, 30 ks 20 – 30 adu, all events = + original image cleaned image removed noise source practically invisible in removed events

  25. LW thick, SNR 1E0102, rev 0803, 30 ks, CCD 4 before / after removal of noisy frames 20 – 30 adu all events same intensity cuts

  26. SNR 1E 0102, rev 803, LW, thick, 30 ks In order to determine the noise properties, some areas have to be ignored: mask for source regions to be avoided for noise determination singles, 0.3-3.0 keV source region mask

  27. SNR 1E 0102, rev 803, LW, thick, 30 ks In order to determine the noise properties, some areas have to be ignored: noisy pixel mask final mask, excluding source regions and noisy pixels

  28. SNR 1E 0102, rev 803, LW, thick, 30 ks unfiltered data singles, 24 adu after noisy frame removal

  29. LW thick, SNR 1E0102, rev 0803, 30 ks = + singles, 20 – 30 adu, frames with 1 or 2 events below 31 adu per frame and CCD singles, 20 – 30 adu, frames with more than 2 events below 31 adu per frame and CCD singles, 20 – 30 adu, all frames

  30. LW thick, SNR 1E0102, rev 0803, 30 ks singles, 120-130 eV unfiltered data after noisy frame removal

  31. epreject

  32. At which stage should “noisy frames” be identified and removed ? raw data  recombined event list raw data:  events not yet recombined  better discrimination of noisy frames  standard processing does not allow removal of frames before epevents  major changes of SAS may be required  time-consuming tasks (epevents..) need to be repeated in case of iterations  screening improves signal/noise ratio only at low energies and would cause unnecessary loss of exposure at higher energies recombined events list:  quick and easy application: optional task at the end of the pipeline processing  number of events within PHA range not available anymore (patterns)

  33. Solution: • separate the determination of noisy frames from their removal ! •  only one change required for pipeline processing: keep the “rawevents.dat” files; • they contain • information about the raw events (no recombination yet performed) • time tags: unique identification of frames possible, also in the final event list ! • noise suppression is based on the removal of complete frames •  no need for repeating the pattern recognition and energy correction, only exposure needs to be updated if frames are removed • one additional task (e.g. “epnoise”) would be sufficient: • filter a copy of the final event listaccording to criteria derived from the “rawevents.dat” files and other input: masks, adu range and cutoff criteria (e.g. discrepancy between observed distribution and poissonian fit) • update the exposure extensions • quick iterations possible • output from source detection could be used for creating masks

  34. optional image mask upper ADU threshold “epnoise” possible implementation: raweventsxx.dat “Final” cleaned event file

  35. Update of EPIC pn noise suppression EPIC Calibration & Operations Meeting Mallorca, Spain, 2009 March 31 – April 1

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