1. GWDAW 16/12/2004 Inspiral analysis of the Virgo commissioning run 4 Leone B. Bosi VIRGO coalescing binaries group on behalf of the VIRGO collaboration

2. Leone B. Bosi –VIRGO Coalescing Binaries Group 2 C4 Standard Configuration C4 Run Planning : Beginning: 24 / 6 / 2004 End:29 / 6 / 2004 C4 Configuration ITF in recombined mode Automatic Alignment on both arms Frequency Stabilization active

3. Leone B. Bosi –VIRGO Coalescing Binaries Group 3 C4 data overview 1 2 3 4 Hardware Injection Software Injection 2 h Burst 2 h Coalescing Binaries Wanted SNR 7,10,20 2 h Burst 2 h Coalescing Binaries SNR 7,10,20 Full Analysis 4 regions

4. Leone B. Bosi –VIRGO Coalescing Binaries Group 4 C4 frequency characterization for CB Frequency band = 60  2000 Hz The cut-in frequency is fixed to 60Hz The cut-off frequency is fixed to 2kHz Sample rate @4kHz

5. Leone B. Bosi –VIRGO Coalescing Binaries Group 5 C4 data and Templates Grid population C4 was quite stable in the science mode period Damir Buskulic Templates bank generated with the Damir Buskulic generator Parameters space [0.9;5] M  Parameters space changes with the sensitivity fluctuation The change in the number of filters in a bank indicates the stability level of the Interferometer

6. Leone B. Bosi –VIRGO Coalescing Binaries Group 6 C4 data and Templates Grid population Detail

7. Leone B. Bosi –VIRGO Coalescing Binaries Group 7 C4 data and Coalescing Binaries Detected Distance Stability The sight distance of a CB can be used to understand the behavior of the interferometer The sight distance was quite stable In particular in the science mode period % variation

8. Leone B. Bosi –VIRGO Coalescing Binaries Group 8 Distributed Signal Analyzer description Distributed Framework composed by 4 type of processes online /offline data Studied to speed up data analysis problems like single versus multiple data plug-insFunctionality are inserted in the parallel framework logic like “plug-ins”, common serial code, avoiding any parallel programming by the user Communication based on MPI and running on a Beowulf cluster

9. Leone B. Bosi –VIRGO Coalescing Binaries Group 9 Matched Filter implementation Template bank is generated h i (t)  H i (  ) distributed on all the processes memory  distributed on all the processes memory x(t)  X(w) Double Whitening The integral is evaluated in each process  in each process for each template h i (t)  for each template h i (t)

10. Leone B. Bosi –VIRGO Coalescing Binaries Group 10 Hardware injection Parallel Analysis 3276 templates Masses range = [ 0.9 ; 5.0 ]M  Minimal match 0.98 % f l = 60 Hz Hardware injections of inspirals and bursts on 5 hours long period 32758 events with SNR>6 Visible all CB events and Bursts too!! Used  2 to discriminate (8 bands)

11. Leone B. Bosi –VIRGO Coalescing Binaries Group 11 Hardware injection with multi-band analysis Multi-band analysis –[60 – 290 – 2000] Hz –[1, 3] M , MM = 95% –518 templates All inspiral injections detected.. and bursts

12. Leone B. Bosi –VIRGO Coalescing Binaries Group 12 High SNR Hardware Injection 4.64 4.59 4.66 C4 Sensitivity was better than the one used to calibrate the injection

13. Leone B. Bosi –VIRGO Coalescing Binaries Group 13 Software Injection Parallel Analysis Results similar to the HW injected All CB associated SNR > 5.5 1 missed with SNR > 6 Bursts trigger like in hardware injection!! Gps time Used  2 to discriminate Missed 3 good events after GPS time SNR count

14. Leone B. Bosi –VIRGO Coalescing Binaries Group 14 Software injection with multi-band analysis 45/46 injections detected with SNR > 6 Good agreement SNR detected / injected

15. Leone B. Bosi –VIRGO Coalescing Binaries Group 15 Analysis of 5 hours long quiet period Period where the CB signals were injected via Software Region was science mode data, reconstructed SNR distribution up to ~14

16. Leone B. Bosi –VIRGO Coalescing Binaries Group 16 Performance and robustness of Merlino Run with 10000 templates 4MB each template –120s @4kHz 110GBMore than 110GB filters data handled Run with 40 CPUs 8 daysin-timeRun continuously for 8 days  in-time h-reconstructionRun continuously in the on-line chain, receiving in input data from on-line h-reconstruction

17. Leone B. Bosi –VIRGO Coalescing Binaries Group 17 Conclusions Experience in Hardware and Software injection during the C4 run C4 interferometer science mode analysis simulated signalsREAL dataDetection of simulated signals in REAL data Detection algorithms and parallel analysis test Good stability of the VIRGO interferometer in terms of coalescing binaries detectionGood stability of the VIRGO interferometer in terms of coalescing binaries detection

18. Leone B. Bosi –VIRGO Coalescing Binaries Group 18

19. Leone B. Bosi –VIRGO Coalescing Binaries Group 19 Distributed Signal Analyzer Merlino description Dynamic Filter algorithm (plug-ins) overlap-add MethodData handled with overlap-add Method Filters (generic) stored in memory Broadcast / asynchronous communicationBroadcast / asynchronous communication  In-time computation

20. Leone B. Bosi –VIRGO Coalescing Binaries Group 20 Data Quality