Results from 2007-08 data unblinding on point source analysis with the EM algorithm

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Results from 2007-08 Results from 2007-08 data unblinding on data unblinding on point source analysis point source analysis with the EM algorithm with the EM algorithm AWG AWG videoconference videoconference meeting meeting 24/03/2011 24/03/2011 (II) (II) J.P Gómez-González AWG videoconference meet. 24/03/2011 0

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AWG videoconference meeting 24/03/2011 (II). Results from 2007-08 data unblinding on point source analysis with the EM algorithm. J.P Gómez-González AWG videoconference meet. 24/03/2011. Outline. Search method review Results from the candidate list search - PowerPoint PPT Presentation

Transcript of Results from 2007-08 data unblinding on point source analysis with the EM algorithm

Page 1: Results from 2007-08 data  unblinding  on point source analysis with the EM algorithm

Results from 2007-08 Results from 2007-08 data unblinding on point data unblinding on point source analysis with the source analysis with the

EM algorithmEM algorithm

AWG AWG videoconference videoconference

meetingmeeting24/03/201124/03/2011

(II)(II)

J.P Gómez-González AWG videoconference meet. 24/03/2011 0

Page 2: Results from 2007-08 data  unblinding  on point source analysis with the EM algorithm

Search method review

Results from the candidate list search

Results for the all sky search

Summary

OutlineOutline

J.P Gómez-González AWG videoconference meet. 24/03/2011 1

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Clustering algorithm

EM free likelihood distribution exampleThe EM algorithm is a clustering algorithm

that analytically maximizes the likelihood

We assume that source events follow a Gaussian distribution

The width of this Gaussian includes the dependence of on the angular resolution of the detector:

In the “free sigma case” the width can change during the maximization process between

reasonable values. Remind that both sigmas are keep as independent parameters.

In the “fixed sigma case” the width do not change and keeps the value extracted from the mc

simulations

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Data sample

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Same events coordinates (checked)~304 days of livetime

2190 events in the sample

Cuts: > -5.4, < 1º, upgoing events

Reconstruction method: aafit v0r8

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Sky map

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Sky map in equatorial coordinates showing the positions of the 2190 events used in the analysis

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EM free candidate list search

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pre-trial

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EM fix candidate list search

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pre-trial

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NIKHEF PS analysis results

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Summary of results

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method source decl ra nsig lik p-value

EM free HESS J1023-575

-57.76 155.83 3.32 4.32 0.18

EM free GX 339 -104.3 -48.79 4.77 2.53 0.10

EM fix HESS J1023-575

-57.76 155.83 2.53 3.25 0.09

EM fix GX 339 -104.3 -48.79 1.77 2.46 0.25

Lik (NIKHEF)

HESS J1023-575

-57.76 155.83 2.65 2.53 0.17

Lik (NIKHEF)

GX 339 -104.3 -48.79 2.18 2.15 0.26

The most significant sources on the candidate list are found by all the “different” methods (compatible results in all cases)

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Clusters from candidate list search

Number of events fitted by the algorithm: 3.32Likelihood ratio value: 4.32P-value: 0.0074 x 24 ~ 17.8% probability (< 1.5)Gaussian width: sx = 1.10º , sy = 0.5º

The gaussian is wider in the RA plane. Sigma parameters (X,Y) are

independent

EM free

HESS J1023-575HESS J1023-575

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Green ellipse: pre-clustering coneBlue ellipse: fitted gaussian

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Clusters from candidate list search

Number of events fitted by the algorithm: 2.53Likelihood ratio values: 3.25P-value: 0.0036 x 24 ~ 8.7 % probability

Now, the sigma values of the Gaussian are fixed

EM fix

HESS J1023-575HESS J1023-575

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Green ellipse: pre-clustering coneBlue ellipse: fitted gaussian

event id: 1 -- decl: -57.839516 -- ar: 157.401820 -- nhit: 52 -- nline: 4 -- run: 27892event id: 2 -- decl: -57.641273 -- ar: 154.210445 -- nhit: 39 -- nline: 8 -- run: 32490event id: 3 -- decl: -57.742114 -- ar: 159.541817 -- nhit: 25 -- nline: 5 -- run: 33486event id: 4 -- decl: -57.535276 -- ar: 156.459877 -- nhit: 70 -- nline: 6 -- run: 33713

Same cluster was found in the 5line analysis, but now we have only one event from that period

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Clusters from candidate list search

Number of events fitted by the algorithm: 4.77Likelihood ratio value: 2.53

EM free

GX 339GX 339

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Green ellipse: pre-clustering coneBlue ellipse: fitted gaussian

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Clusters from all sky search

EM free

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clust decl ra bic nsig

1 -36.71 39.92 7.9 2.89

2 -0.50 43.26 7.1 2.89

clust 1clust 2

Same cluster found in the NIKHEF analysis

P > 95% (BG)

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Clusters from all sky search

EM fix

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Green ellipse: pre-clustering coneBlue ellipse: fitted gaussian

decl ra bic nsig

-0.50 43.28 7.1 2.89

Same cluster found by NIKHEF analysis

event id: 0 -- decl: -0.569987 -- ar: 43.104131 -- nhit: 50 -- nline: 6 -- run: 36133event id: 1 -- decl: -0.566569 -- ar: 42.962954 -- nhit: 42 -- nline: 8 -- run: 37151event id: 2 -- decl: -0.348181 -- ar: 43.711867 -- nhit: 49 -- nline: 6 -- run: 34397event id: 3 -- decl: -2.853588 -- ar: 42.782587 -- nhit: 24 -- nline: 3 -- run: 30437

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J.P Gómez-González AWG videoconference meet. 24/03/2011

Sample from 2007-08 data unblinded

Similar results than previous analysis were found

Candidate list: HESS J1023-575 p ~ 17.8%

All sky: fully compatible with BG hypothesis

SummarySummary

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