GRB Trigger Algorithms From DC1 to DC2

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GRB Trigger AlgorithmsFrom DC1 to DC2

• Nicola Omodei
• Riccardo Giannitrapani
• Francesco Longo
• Monica Brigida

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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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Bands 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 triggerstatistically 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 regions
  background estimated only from that regions
  lightcurve will be dominated by Poisson noise.
  The event rate per region is a few10-2 Hz.
• Bands 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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Norriss method

• They used only one N-event sliding window as the
  first bootstrap step in searching for significant
  temporal-spatial clustering. Compute Log Joint
  (spatialtemporal) 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.

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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 ltlt 10-6/day.
• Additional aspects we will evaluate for on-board
  implementation
• Floating threshold 2-D PSF spatial clustering
  (Galactic Plane)

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Riccardos 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 photons
For outliers
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Nicolas 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/second
Burst photons
GRB050718i
Spectral analysis done with XSPEC (Monica) Light
curves visualized Position in the sky map
visualized
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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!

Background estimation
SkyMap segmentation
On board On board recon (filter) Fast Low
memory consuming
Buffer
On ground Full recon High sensitivity No
restriction on memory/time
Trigger Algorithm
Data storage
Trigger on the counts rate
Likelihood

Outliers
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GRB Spectra

• EventBin XSPEC
• (Francesco tutorial, XSPEC tutorial ..)
• Fitting models power_law / grbm

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• GRB050720a

1634 counts Tstart 176761 Tstop 176880 Ra
128 Dec 65 Flux 2.9 E-6 erg cm-2 s-1
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Power law model
Model powerlawlt1gt 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 powerlawlt1gt
• Model Fit Model Component Param Unit
  Value
• par par comp
• 1 1 1 powerlaw PhoIndex 2.25854
  /- 0.403036E-01
• 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 freedom
• Null hypothesis probability 7.773E-07

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GRB050718i
700 counts Tstart 75415 Tstop 75473 Ra
92 Dec 57 Flux 2.6 E-6 erg cm-2 s-1

• GRB_050718i 75415 / 75474 92 / 57
• Model powerlawlt1gt
• 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

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GRB050718i powerlaw

• ignore -1e5 1e8-
• Model powerlawlt1gt
• 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