Title: GRB Trigger Algorithms From DC1 to DC2
1GRB Trigger AlgorithmsFrom DC1 to DC2
- Nicola Omodei
- Riccardo Giannitrapani
- Francesco Longo
- Monica Brigida
2DC1 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.
3Bands 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.
4Estimating 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.
5Problem 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.
6Norriss 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)
8Riccardos 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
9Some improvements
- Riccardo also compare the distribution of the
counts with the Poisson distribution The GRB are
now really visible!
Outliers
10Some 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
11Nicolas 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!
12The 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.
13The 25 lightcurves
14Comparing 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
15Common 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.
16GRB 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
17GRB Spectra
- EventBin XSPEC
- (Francesco tutorial, XSPEC tutorial ..)
- Fitting models power_law / grbm
181634 counts Tstart 176761 Tstop 176880 Ra
128 Dec 65 Flux 2.9 E-6 erg cm-2 s-1
19Power 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
20powerlaw
- 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
21GRB050718i
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
22GRB050718i 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