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Robotic algorithm of HEND detection of GRBs

Robotic algorithm of HEND detection of GRBs. V. Grinkov, A. Sanin, S. Charyshnikov, A. Kozyrev, M. Litvak, I. Mitrofanov, V. Tret’yakov Institute for Space Research K. Hurley Space Sciences Laboratory, University of California, Berkeley T. Cline NASA/Goddard Space Flight Center.

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Robotic algorithm of HEND detection of GRBs

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  1. Robotic algorithm of HENDdetection of GRBs V. Grinkov, A. Sanin, S. Charyshnikov, A. Kozyrev, M. Litvak, I. Mitrofanov, V. Tret’yakov Institute for Space Research K. Hurley Space Sciences Laboratory, University of California, Berkeley T. Cline NASA/Goddard Space Flight Center

  2. Gamma-Ray Burst HEND Subsystem Profile data are transmitted continuously by 60 s profiles Time resolution: Outer 0.25 s Inner 1 s Energy range: Outer 30 – 1000 keV Inner 60 – 2000 keV Spectra of both detectors consist of 16 channels and have time resolution of 20 s

  3. This is the position where the GRB will be registered GRB phase Afterglow phase t 0 1 second 1 month 1 minute 1 hour 1 day

  4. Three main steps of the algorithm are as follows: I. Background Model, to know the background variations II. Search for Events, to find possible candidates to be GRBs III. Correlation Analysis, to eliminate non-burst events

  5. I. Background model • Smoothing • Weight determination • Weighed smoothing • Return to Step 2 The initial profile is smoothed with 70-bin window to derive a smoothed one

  6. I. Background model • Smoothing • Weight determination • Weighed smoothing • Return to Step 2 In future smoothing each bin will have its weight which is determined by the weight function (upper left) according to the difference between smoothed and initial profiles

  7. I. Background model • Smoothing • Weight determination • Weighed smoothing • Return to Step 2 Derived with the same window, new smoothed profile substitutes the old one

  8. I. Background model • Smoothing • Weight determination • Weighed smoothing • Return to Step 2 Steps 2-3-4 are repeated 20 times until the smoothed profile represents a good background estimate (green line at the picture)

  9. II. Search for events 1. Search for triggered bins and combine them into events 2. Discard short events 3. Stretch boundaries All bins exceeding 6 std. dev. above the background are treated as triggered. Neighboring triggered bins are combined into events.

  10. II. Search for events 1. Search for triggered bins and combine them into events 2. Discard short events 3. Stretch boundaries Events shorter than 3 bins are discarded

  11. II. Search for events 1. Search for triggered bins and combine them into events 2. Discard short events 3. Stretch boundaries 35 bins are added to each side of the event. Some events will merge.

  12. III. Correlation analysis The GRB gives signals both in the outer and inner SC (left), while the anomalous signal is reflected only in the outer (down). The blue lines represent flux in the outer, and the green lines in the inner, detector

  13. Statistics of the registered events

  14. The whole set of GRBs found by the algorithm

  15. An algorithm to search for bursts in HEND data was built. The algorithm triggered 8 real and 24 false events during both quite and noisy data. As an improvement of the algorithm spectral data procession must be added. Conclusions

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