GWDAW 11 - Potsdam, 19/12/2006

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Coincidence analysis between periodic source candidates in C6 and C7 Virgo data

C.Palomba (INFN Roma) for the Virgo Collaboration

• I report on the ongoing work done in collaboration with Pia Astone and Sergio Frasca

• Blind analysis of the data of runs C6 and C7 to search for gravitational signals emitted by isolated rotating neutron stars

• Selection of candidates in the two data sets and coincidences between them.

• Injection of simulated signals

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‘Blind’ search

• Assumes source position, frequency and spin-down are not known • The vast majority of neutron stars is not visible in the EM band

• It is rather unlikely that known NS (pulsars) emit detectable signals• Local population of neutron stars must be taken into account• Blind searches cannot be performed with optimal methods due to the huge number of points in the parameter space

• Hierarchical procedures strongly cut the needed computing power at the cost of a small reduction in sensitivity

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h-reconstructed data

Data quality

SFDB

Average spect rum estimation

peak map

hough transf.

candidates

peak map

hough transf.

candidates

coincidences

coherent step

events

Hierarchical method for ‘blind’ searches

Astone, Frasca, Palomba, CQG 22, S1197 2005

Data quality

SFDB

Average spect rum estimation

Frasca, Astone, Palomba, CQG 22, S1013 2005

Palomba, Astone, Frasca, CQG 22, S1255 2005

Presentation at MG11

The procedure involves two or more data sets belonging to a single or more detectors

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Parameter space

• observation time

• frequency band

• frequency resolution

• number of FFTs

• sky resolution

• spin-down resolution

Hz 00095367.0f

(@1050Hz) deg 3.0)50(@ deg5.7, Hz

Hz/s1058.1

(@1050Hz)yr 2100(@50Hz)yr 100 8

max

f

Hz/s1076.1 10N :C7

Hz/s1006.4 40N :C69

sd

10sd

f

f

HzHz 105050

daysTCdaysTC obsobs 37.3:7 87.13:6

556 :7

2286 :6

FFT

FFT

NC

NC

f

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~1013 points in the parameter space are explored for each data set

sTFFT 576.1048

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• On each Hough map (corresponding to a given frequency and spin-down) candidates are selected putting a threshold on the CR

• The choice of the threshold is done according to the maximum number of candidates we can manage in the next steps of the analysis

Candidates selection

• In this analysis we have used 8.3thrCR

• Number of candidates found:

C6: 922,999,536 candidates

C7: 319,201,742 candidates

map

mapnCR

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MC 1st violin mode

• ‘Effective’ (i.e. after selection of candidates) sensitivity loss respect to optimal analysis: C6: 2.4 C7: 1.8 • False alarm probability: C6: C7:

4101.1 4107.1

• Still candidates excess at many frequencies, even if some cleaning has been done

7red line: theoretical distribution

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‘quiet’ band

‘disturbed’ band

Many candidates appear in ‘bumps’ (at high latitude), due to the short observation time, and ‘strips’ (at low latitude), due to the symmetry of the problem

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Coincidences

• Number of coincidences: 2,700,232

• Done comparing the set of parameter values identifying each candidate

• To reduce the false alarm probability; reduce also the computational load of the coherent “follow-up”

• False alarm probability: 7102.2 band 1045-1050 Hz

• Coincidence windows: 2 ,2 ,0 ,1 ff

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Detection of injected signals

• We make coincidences between candidates found in C7 data + injections and the injected signals

• 66 signals injected in C7 data, with frequency in [50,550]Hz and no spin-down, to study efficiency and accuracy in parameter estimation of the incoherent step

• Coincidence windows:

1964 candidates

15 ,3 ,0 ,1 ff

many sources undetected

green curve: expected C7 sensitivity

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• To check if the short observation time plays a role, we dilate time by a factor 80 (and reduce spin-down of injected signals by the same amount)

5257 candidates

• Good agreement with the expected sensitivity.

• Accuracy in latitude is only slightly affected by the length of the observation time

• Longer time interval increases the detection efficiency

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• Given two or more data sets, we can suitable mix them in order to produce data sets covering a larger time interval

time

time

• If the two sets are created with nearly equal sensitivity, we have a further gain

See presentation at MG11 for more details

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Good correlation between signals amplitude and CR of candidates

Strongest sources are detected with more accurate position

Worse accuracy for low frequency sources (due to lower resolution)

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• As already noted, sources at low ecliptic latitude are detected with worse accuracy. This is basically independent on the observation time.

• We expect to have an improvement by using the adaptive Hough transform, which breaks the symmetry respect to the ecliptic.

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Conclusions

• Well established procedure for going from data to candidates

• We make coincidences to reduce false alarm and computational load of the coherent step

• Need for stretch of data covering a time interval as large as possible to have better detection efficiency

• Uncertainty in latitude will be reduced by using the adaptive Hough transform

• Need to extend the injections to non zero spin-down

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Spare slides

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N=1N=2N=3N=4

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