udine 11- 03 - 2004 nicola omodei 1 grb trigger algorithms from dc1 to dc2 nicola omodei riccardo...
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1Udine 11- 03 - 2004 Nicola Omodei
GRB Trigger AlgorithmsFrom DC1 to DC2
Nicola Omodei
Riccardo Giannitrapani
Francesco Longo
Monica Brigida
2Udine 11- 03 - 2004 Nicola Omodei
DC1 Closeout status
• Many people involved in GRB detection: I think that the generation of GRB has been a success!
• 4(+1) groups have been working on GRB detections:– David Band
– Jay Norris & Jerry Bonnell
– Riccardo Giannitrapani et al.
– Nicola Omodei
– Tune Kamae et al.
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Band’s method• Break up sky in instrument coordinates into regions, and apply rate triggers
to each region. The regions are ~PSF in size (builds in knowledge of the instrument).
• Use two (or more) staggered regions so that the burst will fall in the interior of a region.
• Rate trigger—statistically significant increase in count rate averaged over time and energy bin.
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Estimating the Background
• The rate trigger requires an estimate of the background (=non-burst event rate). Typically the background is estimated from the non-burst lightcurve.
• BUT here the event rate is so low that a region’s background estimated only from that region’s lightcurve will be dominated by Poisson noise. The event rate per region is a few×10-2 Hz.
• Band’s current method is to average the background over the FOV, and apportion it to each region proportional to the effective area for that region.
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Problem with Background Estimation
• Problem: On short (~100 s) timescales the background is NOT uniform over the FOV. The ridge of emission along the Galactic plane causes many false triggers.
• Solution (not implemented yet): Better model of the background.
Region with false trigger •In the ~6 days of DC1
data,He found 16 bursts and 29 false triggers.•Note that his spatial grids extend to inclination angles of 65º and 70º.•The software He used was all home-grown IDL procedures.
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• They used only one N-event sliding window as the first bootstrap step in searching for significant temporal-spatial clustering. Compute Log {Joint (spatial*temporal) likelihood} for tightest cluster in window:
Log(P) = Log{ [1 – cos(di)] / 2 } + Log{ 1 – (1 + Xi) exp(-Xi) }
• Their work is somewhat at 45 to main DC1 purposes. But DC1 set us up with all the equipment necessary to proceed:
• Future emphasis will move to on-board recon problems: highest accuracy real-time triggers & localizations.
Norris’s method
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• Very sensitive trigger — incorporates most of the useful information.
• 17 detections: 11 on Day 1; 6 on Days 2-6. Some bright, some dim.
• No false trigger. Formal expectation any detection is false << 10-6/day.
• Additional aspects we will evaluate for on-board implementation:
• Floating threshold; 2-D PSF; spatial clustering (Galactic Plane)
8Udine 11- 03 - 2004 Nicola Omodei
Riccardo’s method• The aim of the Riccardo talk was to present a analysis tool call “R”
• He presented also an application of this tool for GRB searching, based on the quantile analysis.
• He looks for outliers in the distribution of the count rate
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Some improvements
• Riccardo also compare the distribution of the counts with the Poisson distribution: The GRB are now really visible!
Outliers
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Some other improvements
• Another way to see the outliers is looking at the (smoothed) counts map for (RA, TIME) coordinates (or for (DEC,TIME));
For all photonsFor outliers
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Nicola’s method• First algorithm based
on the trigger on the differential count rate (this get rid of the fluctuation of the background due to the galactic plane).
• Very easy and fast algorithm!
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The division of the sky
• The same algorithm can be applied separately in sub region of the galactic map. This substantially reduces the background (non-burst events).
5 x 5 array reduces the “background” by a factor 25.
Also faint burst can be detectable.
Direct (70˚ x 36˚) information on the localization.
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The 25 lightcurves
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Comparing the results
Generated 11 GRBs with spikes with more than 2 photons/secondBurst time David JJ Riccardo Nicola Nph
3000 * * * * 827000 * * 811000 * * * 211500019000 * * 3223000 * * * 1327000 * * * * 353100035000 * * 233900043000 * * * * 10747000 * * 41510005500059000630006700071000 * * * * 7175000 * * * * 7537900083000 * * * * 50
Spectral analysis done with XSPEC (Monica)Light curves visualizedPosition in the sky map visualized
Burst photons
GRB050718i
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Common features and diversities• All of us triggered on the counts rate (in different ways): the gamma
background (simulated) is low compared with the burst flux.• Both faint bursts (few tens of photons) and bright bursts (some
hundreds of photons) have been successfully detected.• A big improvement of the burst trigger rate has been reached by
dividing the sky map in smaller region. This procedure represents a big advantage in terms of background “reduction”.
• David divide the sky map using instrument coordinates, maybe this is the reason of so many false triggers.
• Nicola, Jay and Jerry used galactic coordinates: no false trigger.• Nicola developed a simple (and fast) algorithm and detect burst as
much as Jay and Jerry did with more complicated algorithms.• Riccardo pointed out that one of the burst vanishes if the standard
cuts are applied to the data. This means that with with a realistic background which requires a realistic background filter, some burst photons will be killed by the filter.
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GRB Trigger, Alert & DC2• On-board vs on-ground trigger algorithms. GBM comparison!• Develop a common interface for the burst alert algorithms• Better simulation of the background (including particles)• Background estimation• The development in other environments (IDL,Matlab,R, ROOT stand alone macros), is
very useful, BUT the key point for the DC2 will be the development of science tools!
On boardOn board recon (filter)
Fast & Low memory consuming
On groundFull recon
High sensitivityNo restriction on memory/time
Data storage
Buffer
Trigger AlgorithmTrigger on the counts rate
Likelihood …
Background estimation
SkyMap segmentation
Outliers
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GRB Spectra
EventBin + XSPEC(Francesco tutorial, XSPEC tutorial …..)
Fitting models: power_law / grbm
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GRB050720a
1634 countsTstart: 176761Tstop: 176880Ra: 128Dec: 65Flux: 2.9 E-6 erg cm-2 s-1
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Power law model
Model: powerlaw<1> Model Fit Model Component Parameter Unit Value par par comp 1 1 1 powerlaw PhoIndex 1.74358 +/- 0.198269E-01 2 2 1 powerlaw norm 6.20041 +/- 1.32153 --------------------------------------------------------------------------- --------------------------------------------------------------------------- Chi-Squared = 849.3165 using 8 PHA bins. Reduced chi-squared = 141.5527 for 6 degrees of freedom Null hypothesis probability = 0.00
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powerlaw
ignore **-1e5 1e8-**
Model: powerlaw<1>Model Fit Model Component Param Unit Valuepar par comp1 1 1 powerlaw PhoIndex 2.25854 +/- 0.403036E-012 2 1 powerlaw norm 12150.0 +/- 6275.72----------------------------------------------------------------------------------------------------------------------------------------------Chi-Squared = 36.43491 using 7 PHA bins.Reduced chi-squared = 7.286983 for 5 degrees of freedomNull hypothesis probability = 7.773E-07
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• GRB_050718i 75415 / 75474 ; 92 / 57
• Model: powerlaw<1>• Model Fit Model Component Parameter Unit Value• par par comp• 1 1 1 powerlaw PhoIndex 1.79878 +/-
0.280451E-01• 2 2 1 powerlaw norm 18.8932 +/- 5.67197• ---------------------------------------------------------------------------• ---------------------------------------------------------------------------• Chi-Squared = 212.8927 using 7 PHA bins.• Reduced chi-squared = 42.57854 for 5 degrees of
freedom• Null hypothesis probability = 4.905E-44
GRB050718i700 countsTstart: 75415Tstop: 75473Ra: 92Dec: 57Flux: 2.6 E-6 erg cm-2 s-1
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GRB050718i powerlaw
• ignore **-1e5 1e8-**
• Model: powerlaw<1>• Model Fit Model Component Parameter Unit Value• par par comp• 1 1 1 powerlaw PhoIndex 2.16394 +/- 0.472441E-
01• 2 2 1 powerlaw norm 2908.87 +/- 1490.38• ---------------------------------------------------------------------------• ---------------------------------------------------------------------------• Chi-Squared = 2.117105 using 7 PHA bins.• Reduced chi-squared = 0.4234209 for 5 degrees of freedom• Null hypothesis probability = 0.833