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ILIAS WP1 – Cascina 13.11.2006 IGEC1 1997-2000 – First experience using the data of 5 bar detectors: ALLEGRO, AURIGA, EXPLORER NAUTILUS and NIOBE. – 1460.

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Presentation on theme: "ILIAS WP1 – Cascina 13.11.2006 IGEC1 1997-2000 – First experience using the data of 5 bar detectors: ALLEGRO, AURIGA, EXPLORER NAUTILUS and NIOBE. – 1460."— Presentation transcript:

1 ILIAS WP1 – Cascina 13.11.2006 IGEC1 1997-2000 – First experience using the data of 5 bar detectors: ALLEGRO, AURIGA, EXPLORER NAUTILUS and NIOBE. – 1460 days over 4 years – 1322 days single antenna – 713 days two-fold coincidences – 178 days three-fold coincidences – 29 days four-fold coincidences IGEC2 from May 2004  … NIOBE no more in operation. First exchanged period May-November 2005 when no other observatory was operating. A larger amount of data is available for further analysis. IGEC

2 ILIAS WP1 – Cascina 13.11.2006 A DIRECTIONAL 4-ANTENNAE OBSERVATORY The four antennas receive an identical signal, independently from the source

3 ILIAS WP1 – Cascina 13.11.2006 SENSITIVITY OF PRESENT DETECTORS

4 ILIAS WP1 – Cascina 13.11.2006 IGEC 1 1997-2000 IGEC 2 2004 - …. FROM IGEC1 TO IGEC2

5 ILIAS WP1 – Cascina 13.11.2006 The analysis uses lists of candidate events selected (SNR>4-4.5) using filters matched for  signals. Auxiliary information connected to the data quality are also exchanged among the groups (the exchange protocol is analogous to the one used in IGEC1 analysis) The search parameters (search threshold, coincidence window and working periods) are fixed “a priori” by a blind analysis of the background characteristics The preliminary analysis is blind as each group add a “secret time shift” to the time of the candidate events. An upper limit at the present sensitivity of the bar detectors is not an interesting scientific result therefore the analysis was tuned to a possible detection requiring an identification of possible candidates with high confidence fixing the three-fold false alarm to 1 over 100 years To cross check the filter pipeline we also exchanged raw data. DATA ANALYSIS METHODS

6 ILIAS WP1 – Cascina 13.11.2006 The first exchanged data set starts on Friday May 20 and ends Tuesday Nov 15, 2005. The collaborations have exchanged the data of AURIGA EXPLORER and NAUTILUS in the period April-July 2006. ALLEGRO had difficulties in producing data: we agreed to proceed anyway and use ALLEGRO data for a follow-up investigation of any candidate found. The analysis results were public at the end of September 2006 FIRST ANALIZED PERIOD

7 ILIAS WP1 – Cascina 13.11.2006 OPERATION TIME – MAY 20 –NOV 15, 2005 (AURIGA- EXPLORER- NAUTILUS) no detector 0.6 days Single 3.6 days Double 45.0 days Triple 130.8 days ALAUEXNA AL0 AU0172.9 EX0151.8158.0 NA0150.2135.3155.0 96 %87 %86 % 180 days Data from ALLEGRO to be added HIGH DUTY FACTOR days of operation

8 ILIAS WP1 – Cascina 13.11.2006 AMPLITUDE OF THE EXCHANGED DATA MAY – DEC 2005 SNR  4.5 per AURIGA SNR  4.0 per EXPLORER E NAUTILUS

9 ILIAS WP1 – Cascina 13.11.2006 AMPLITUDE OF THE EXCHANGED DATA SNR  4.5 per AURIGA SNR  4.0 per EXPLORER E NAUTILUS

10 ILIAS WP1 – Cascina 13.11.2006 The sensitivity is stationary There is an enhancement of about a factor 3 respect to IGEC 1 SEARCH THRESHOLD Percentage of time with the adaptive threshold T smaller than H

11 ILIAS WP1 – Cascina 13.11.2006 THREE-FOLD PERIOD VS AMPLITUDE THRESHOLD Amplitude H t [10 -21 Hz -1 ]

12 ILIAS WP1 – Cascina 13.11.2006 TIME UNCERTAINTY AURIGA For EXPLORER and NAUTILUS the  t was fixed at 10 ms Coincidence window IGEC 1 style |t 1 –t 2 | < 4.47 sqrt [  2 (t 1 ) +  2 (t 2 )] using Byenaimé-Tchebychev false dismissal < 5%

13 ILIAS WP1 – Cascina 13.11.2006 TWO_FOLD COINCIDENCE DISTRIBUTION example AU -EX The distribution of the coincidence is poissonian

14 ILIAS WP1 – Cascina 13.11.2006 THREE-FOLD COINCIDENCES BACKGROUND ALL THE EXCHANGED DATA SNR  4.5 AU SNR  4.0 EX-NA Time shift AURIGA=0

15 ILIAS WP1 – Cascina 13.11.2006 THREE-FOLD COINCIDENCES BACKGROUND ALL THE EXCHANGED DATA SNR  4.5 AU SNR  4.0 EX-NA Entries 1879417 Mean 2.415  2 /ndf 12.6 /12

16 ILIAS WP1 – Cascina 13.11.2006 CHOICES IN THE ANALYSIS We decided to use in the analysis only three-fold coincidences All the exchange periods was considered for analysis We decided to perform one composite search made by the OR among 3 different data selections : SNR > 4.95 for AU, EX e NA 0.396 false alarm/century target signals centered in the EX-NA peaks SNR > 7.00 for AU, SNR>4.25 per EX e NA 0.572 false alarm/century target signals barely detectable by EX and NA common thresholds IGEC1-style: thresholds a 1.3, 1.4, 1.5,..., 3.0 x 10 -21 /Hz 0.134 false alarm/century targets  -like signals

17 ILIAS WP1 – Cascina 13.11.2006 DATA SELECTION 1: Common SNR threshold DATA SELECTION 2: Different SNR threshold

18 ILIAS WP1 – Cascina 13.11.2006 CUMULATIVE FALSE ALARM DISTRIBUTION of the 3 data selections The accidental coincidences are the union of the accidental coincidences for each data selection counting only once the repeated accidental coincidences. 3.63  10 -3 over 130 days 1.0 false alarm/century 1   0.02  10 -3 Systematic errors  0.1  10 -3

19 ILIAS WP1 – Cascina 13.11.2006 TEST THE NULL HYPOTHESIS The null hypothesis will be rejected if at least one coincidence is found in zero lag analysis. If null hypothesis is not confirmed: the coincidence is not explained by accidental coincidences with 99.637%  0.006% (  3  ); the coincidence can be due by correlated signal/noise in the two detectors a follow-up a posteriori analysis is necessary to characterize the coincidences

20 ILIAS WP1 – Cascina 13.11.2006 FOLLOW-UP PLANNED ANALYSIS Additional checks for mistakes in the network analysis; it will not affect the confidence of the rejection of the null; the follow-up results will be interpreted in terms of likelihood or “degree of belief” (subjective confidence) by the collaboration; investigation on h(t) data will try to discriminate among known possible sources (gravitational waves, electromagnetic or seismic disturbances, …). The h(t) data will be filtered to implement network searches based on cross-correlation and wavelet transform; Data from ALLEGRO will be added, in particular h(t) and list of candidates. The improvement in false alarm rate will be estimated, but it is difficult to take into account the efficiency of detection; IGEC2 will investigate on simultaneous observations by other kind of detectors (neutrinos, gamma, x …).

21 ILIAS WP1 – Cascina 13.11.2006 ANALYSIS RESULTS On September 25, once defined all the analysis parameters, the secret time shifts were exchanged No candidate event was found and the null hypothesis was not rejected One of the data subset can be used to calculate an upper limit to be compared with the previous one of IGEC 1 95% coverage Burst amplitude [Hz -1 ] Burst Rate [year -1 ]

22 ILIAS WP1 – Cascina 13.11.2006 An observatory made of 3 resonant detectors is presently capable of high duty cycle and low false alarm. Single candidates with very high level of confidences can be identify at low SNR level. The blind analysis used made the statistical interpretation non-controversial Periods of data, longer then the one analyzed, are available and IGEC2 was requested to collaborate with LIGO in S5 run FINAL REMARKS

23 ILIAS WP1 – Cascina 13.11.2006 DATA QUALITY: DISTRIBUTIONS OF EVENTS Few events/day with SNR>6 Few very large events (SNR>30) on the whole period

24 ILIAS WP1 – Cascina 13.11.2006 AU-SNR=4.95 34598 NA-SNR=4.95 42028 EX-SNR=4.95 29217 NA-SNR=4.25 351375 EX-SNR=4.25 245000 AU-SNR=8.00 552 AU-SNR=7.00 790

25 ILIAS WP1 – Cascina 13.11.2006 # TRIPLES>0 17121189 # TRIALS 19355600 COMMON SEARCH THRESHOLS AU-SNR=4.95 NA-SNR=4.95 EX-SNR=4.95 AU-SNR=8.00 NA-SNR=4.25 EX-SNR=4.25 AU-SNR=7.00 NA-SNR=4.25 EX-SNR=4.25 COMMON SEARCH THRESHOLS 9280 [0.134 FA/C] 14751535177 AU-SNR=4.95 NA-SNR=4.95 EX-SNR=4.95 27368 [0.396 FA/C] 316515 AU-SNR=8.00 NA-SNR=4.25 EX-SNR=4.25 26664 [0.385 FA/C] 26664 AU-SNR=7.00 NA-SNR=4.25 EX-SNR=4.25 39507 [0.573 FA/C]

26 ILIAS WP1 – Cascina 13.11.2006  Set a confidence interval on the estimated number of coincidences due to any source of correlated noise or signal. It will be conservative, i.e. ensuring a minimum coverage.  confidence belt construction: Based on Feldman&Cousins construction; the noise model is a Poisson distribution fitting the empirical estimates of the accidental coincidences; to take into account uncertainties in the noise model, consider the union of the confidence belts given by the mean noise b  3  b = 0.00364  0.00006 events ncnc 90% coverage L H 95% coverage L H 002.4403.09 10.114.360.055.14 20.535.910.366.72

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