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GRB Trigger Algorithms From DC1 to DC2

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Title: GRB Trigger Algorithms From DC1 to DC2


1
GRB Trigger AlgorithmsFrom DC1 to DC2
  • Nicola Omodei
  • Riccardo Giannitrapani
  • Francesco Longo
  • Monica Brigida

2
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.

3
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.

4
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.

5
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.

6
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.

7
  • 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)

8
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

9
Some improvements
  • Riccardo also compare the distribution of the
    counts with the Poisson distribution The GRB are
    now really visible!

Outliers
10
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
11
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!

12
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.
13
The 25 lightcurves
14
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
15
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.

16
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
17
GRB Spectra
  • EventBin XSPEC
  • (Francesco tutorial, XSPEC tutorial ..)
  • Fitting models power_law / grbm

18
  • GRB050720a

1634 counts Tstart 176761 Tstop 176880 Ra
128 Dec 65 Flux 2.9 E-6 erg cm-2 s-1
19
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
20
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

21
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

22
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
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