November, 2009 STAC - Data Analysis Report 1 Data Analysis report November, 2009 Gianluca M Guidi Università di Urbino and INFN Firenze for the Virgo Collaboration

November, 2009 STAC - Data Analysis Report 2 LSC-Virgo scientific groups are concluding most of the searches on S5/VSR1 data. Papers with the scientific results are being written and circulated inside the scientific groups and/or the collaborations: All-sky Low Mass CBC All-sky High Mass CBC GRB –CBC All-sky Bursts GRB – Bursts paper: “Search for gravitational-wave bursts associated with gamma-ray bursts using data from LIGO Science Run 5 and Virgo Science Run 1 “ has been submitted to ApJ Virgo is strongly working on Data Qualities, vetoes and noise hunting for S6/VSR2. This is fundamental as glitches are one of the main limiting factor of any search. S6/VSR2 analysis is on-going with a strong partecipation of Virgo

November, 2009 STAC - Data Analysis Report 3 Contents All the results are preliminary and not still vetted to be diffused outside the LSC-Virgo collaborations  Here we present some highlights  the all-sky search for Bursts  the all-sky search Low Mass CBC  A quick look at the status of the work on DQ, vetoes and noise

November, 2009 STAC - Data Analysis Report 4 The all-sky paper is ready and has already circulated in the collaborations All-sky Bursts S5/VSR1

November, 2009 STAC - Data Analysis Report 5 We used simulated signals to evaluate the sensitivity of the search:  NS gravitational collapse to BH and BH ringdown simulated waveforms (Baiotti et al.). These are signals with a high frequency content, in the region where the IFOs are less sensitive  Obtain directely the efficiency of the search as a function of the distance All-sky Bursts S5/VSR1

November, 2009 STAC - Data Analysis Report 6  Sine-Gaussian waveforms  Gaussian waveforms  Harmonic ringdown signals  Band-limited white noise signals All-sky Bursts S5/VSR1 Neutron-star fundamental modes, Supernova Merger of two Black Holes Baker et al Class. Quant. Grav. 24, S25-S31 (2007)

November, 2009 STAC - Data Analysis Report 7 Obtain the sensitivity of the search as a function of h rss : All-sky Bursts S5/VSR1 h rss 50% as low as 5.6e-22 Hz -1

November, 2009 STAC - Data Analysis Report 8 Astrophysical sensitivities of the search:  Amount of mass converted in GW detected with 50% efficiency: All-sky Bursts S5/VSR1 2.1e-8 M o at 10 kpc 0.05 M o at 16 Mpc (Virgo cluster) f 0 =153 Hz Q=9 Core-collapse supernovae f 0 =940 Hz High Q Could emit 8e-5 M o (optimistic limit) Detectable at 30 kpc

November, 2009 STAC - Data Analysis Report 9 Upper Limits on the rate R  Combined S5y1 and S5y2/VSR1 UL at 90% confidence level for strong GW 90% UL on the rate density R for SG, standard candle with E GW = M o c 2 All-sky Bursts S5/VSR1 2.0 – 2.2 yr -1

November, 2009 STAC - Data Analysis Report 10 Astrophysical interpretation:  Example of limit on the rate density of binary BHs mergers (not rescaled for circular polarisations and inclination angle) All-sky Bursts S5/VSR1 3.6%M tot in GW f = 16 kHz (M o /M tot ) [Berti 07; Hannam 09] R  R (M o /M GW ) 3/2 M tot = 100 M o R = 9e-8 yr -1 Mpc -3 Consistent with the theoretical rates from population synthesis models

November, 2009 STAC - Data Analysis Report 11 The search is near conclusion. A first draft of the paper is being circulated in the CBC science group. Low Mass CBC S5/VSR1

November, 2009 STAC - Data Analysis Report 12 4 interferometers = 4 data sets Low Mass CBC S5/VSR1 For each ifo: generate a template bank 2PN to cover the search mass range Example for « Low Mass » search (2-35 Msun) Match filtering: keep triggers above a threshold Signal To Noise Ratio threshold Require coincidence between interferometers (in mass and in time) → Reduce background of accidental coincidences Apply vetoes: - Instrumental vetoes (poor data quality times) -Signal based vetoes (ex:  ² test): check for consistency between signal in the data and detected waveform Surviving coincident triggers Need to estimate false alarm rate of surviving triggers  estimate rate of accidental coincidence by performing “time-slide” experiments: Introduce unphysical relative time shifts btw detectors before applying the coincident stage of the pipeline. Interesting candidate events (or loudest ones) are submitted to a detection checklist procedure. Perform software injections of simulated GW signals to evaluate the pipeline efficiency If no detection, calculate upper limits on event rates

November, 2009 STAC - Data Analysis Report 13 Targetted sources Neutron Star - Neutron Star (BNS) Neutron Star - Black Hole (NSBH) Black Hole - Black Hole (BHBH) Mass range 2-35 M o in total mass, with component mass from 1 to 34 M o for LIGO 2-3 M o in total mass of equal mass binaries for Virgo Low frequency cutoff 40 Hz for LIGO IFOs 60 Hz for Virgo Inspiral horizon distance: Low Mass CBC S5/VSR1

November, 2009 STAC - Data Analysis Report 14 The integration of Virgo in the pipeline augmented the possible combinations of IFOs in coincident Science Mode  A new detection ranking statistic was applied to take into considerations the different noise background in the different mass regions and the different efficiency in detecting GW of certain IFOs combinations wrt others One of the important result of this work is in fact the integration of Virgo in the pipeline, which is ready to be used on S6/VSR2 data Low Mass CBC S5/VSR1 No GW were identified. 90% confidence UL combined with a prior S5 result were derived on the rate of coalescences: ( Mo) ( Mo) ( Mo)

November, 2009 STAC - Data Analysis Report 15 Low Mass CBC S5/VSR x x x x x x x x x BNSBHNSBBH Our UL Optimistic estimate Best estimate

November, 2009 STAC - Data Analysis Report 16 Software injections of simulated NS-BH and BH-BH spinning binaries were done to evaluate the efficiency of the search wrt spinning binaries signals Low Mass CBC S5/VSR1  Our templates are for non-spinning binaries, hence the UL are slightly worser.

November, 2009 STAC - Data Analysis Report 17 To study and remove bad quality data :  The Scientist on Shift have the duty to report any unusual ITF behavior (  Logbook entry)  The online DQ monitors flag environmental instabilities  Offline Kleine Welle-based vetoes are produced  Weekly Glitch shifts have been set up : loudest glitches are investigated individually Data Quality and Glitches Example of a day with very good quality data :  Omega trigger rate ~ 0.5 Hz  SNR > 10 trigger rate ~ 2/hour  Most of high-SNR events are DQ-flagged

November, 2009 STAC - Data Analysis Report 18 DQ performances are evaluated on a week basis (updated twice a day) :  Against MBTA triggers : »  Against Omega triggers : » Data Quality and Glitches For each DQ flag, is computed :  The dead-time  The use percentage  The efficiency  The SNR distribution plot  The SNR vs. time plot DQ safety is evaluated online : »  So far, no unsafe DQ flag has been found

November, 2009 STAC - Data Analysis Report 19 Glitch shifts have been set up : loudest glitches are investigated individually Two main goals of the glitch shifts :  To check that the online DQ flags do their job  To investigate the source of the loudest glitches (to correct / flag them) Data Quality and Glitches Main tool : Omega scan  At Cascina ~2 scans are run every hour on the loudest event of Omega and MBTA

November, 2009 STAC - Data Analysis Report 20 Glitch investigation  A fraction of the glitches have a clear explanation given by the scans (seismic, dust,...) : »Most of them are flagged online by a DQ. Data Quality and Glitches Glitches show correlations with other channels: Efforts are made to understand these correlations KW-based vetoes should be able to flag some of these glitches

November, 2009 STAC - Data Analysis Report 21 We presented two LSC/Virgo analysis on S5/VSR1 data with their astrophysical implications Virgo work on DQ, vetoes is fundamental for assessing the sensitivity of the searches. On-line DQ production is required for S6/VSR2 on-line analysis S6/VSR2 on-line and off-line analysis are going-on as reported in the DA report for the STAC. Conclusions

November, 2009 STAC - Data Analysis Report 22 END