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Current status of burst gravitational wave analysis of TAMA300 with ALF filter

Current status of burst gravitational wave analysis of TAMA300 with ALF filter. T.Akutsu, M.Ando, N.Kanda, D.Tatsumi, S.Telada, S.Miyoki, M.Ohashi and TAMA collaboration. GWDAW10 UTB Texas 2005 Dec. 13. Contents. Target Source ALF filter Flow of analysis Summery. Trigger rate

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Current status of burst gravitational wave analysis of TAMA300 with ALF filter

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  1. Current status of burst gravitational wave analysis of TAMA300 with ALF filter T.Akutsu, M.Ando, N.Kanda, D.Tatsumi, S.Telada, S.Miyoki, M.Ohashi and TAMA collaboration GWDAW10 UTB Texas 2005 Dec. 13

  2. Contents • Target Source • ALF filter • Flow of analysis • Summery • Trigger rate • Detection efficiency • Result GWDAW10@UTB, Texas 2005 Dec. 13

  3. ITarget Source GW RSS amplitude of sources located at 100pc GW RSS amplitude of sources located at the Galactic center Target Gravitational Waves • Burst GW signal from Supernovae Explosion • Time duration ~100msec • Spike-like waveform • GW RSS amplitude and Detector noise level Simulation signals A&A393 523 (2002) GW root sum square (RSS) amplitude GWDAW10@UTB, Texas 2005 Dec. 13

  4. II ALF filter (Alternative Linear Fit filter) Basis idea of slope filter A slope value of a raw of data (N samples) is used to trigger an event. In this work, window size N = (0.4, 0.6, 0.9) [msec] C.Q.G. 22 (2005) S1303 GWDAW10@UTB, Texas 2005 Dec. 13

  5. III Flow of analysis This test is based on distribution of the filer output. • AC line • Violin mode • Calibration peak RAWDATA window Filter Output Trigger List Trigger List Event List Flow of analysis Conditioning ALF filter Time scale veto C.Q.G. 20 (2003) S697-S709 Monitor Veto GWDAW10@UTB, Texas 2005 Dec. 13

  6. Trigger rate Analysis time 187.3 hours Rejected time by veto 4.4 hours Total 182.9 hours Results of veto Analysis data DataTaking9 of TAMA300 (Nov.2003-Jan.2004) • Trigger rate of DT9 GWDAW10@UTB, Texas 2005 Dec. 13

  7. Detection efficiency We set threshold to be , which corresponds at the level of 0.51 events/day with 90% confidence level. • Result of simulation Preliminary Simulation • Monte Carlo simulation of 26signals Injected signals 26 kinds of signals A&A393 523 (2002) GWDAW10@UTB, Texas 2005 Dec. 13

  8. Detection efficiency • Simulation result of Each type signal Type I Regular collapse Type II Multiple bounce collapse Type III Rapid collapse Preliminary Simulation Waveform depends on the type of signal. GWDAW10@UTB, Texas 2005 Dec. 13

  9. Result Preliminary 0.62 events/day Rate • Rate [events/day] with confidence level 90% • Rate GWDAW10@UTB, Texas 2005 Dec. 13

  10. IV Summery • We implemented the time scale veto and the monitor veto in order to remove fake events • Detection efficiency was evaluated by Monte Carlo simulation. • As a result, we obtained 0.62 events/day at Summery Future work • Improvement of data conditioning • Adjustment of filter parameters • Coincidence analysis for reduction of fake events GWDAW10@UTB, Texas 2005 Dec. 13

  11. Simulation Simulation about Our Galaxy Source-distribution model Astron.Journal 125 1958 (2003) sky-survey observation • antenna pattern • sensitivity an event location • polarization of a source Preliminary GWDAW10@UTB, Texas 2005 Dec. 13

  12. Time scale veto This test is based on distribution of the filer output. Time scale test C.Q.G. 20 (2003) S697-S709 ♣ Stability of the noise level ♣ Gaussianity GWDAW10@UTB, Texas 2005 Dec. 13

  13. Time scale veto Time scale test example long small Time scale Amplitude long small Time scale GWDAW10@UTB, Texas 2005 Dec. 13

  14. Time scale veto Longest time scale signal GWDAW10@UTB, Texas 2005 Dec. 13

  15. Detection efficiency Simulation Injected signals A&A393 523 (2002) 26 signals GWDAW10@UTB, Texas 2005 Dec. 13

  16. Result Rate • Rate [events/day] with confidence level 90% Preliminary type1 0.53 events/day 0.62 events/day Type2,3 0.73 events/day GWDAW10@UTB, Texas 2005 Dec. 13

  17. Simulation about Our Galaxy Source-distribution model Astron.Journal 125 1958 (2003) sky-survey observation Preliminary GWDAW10@UTB, Texas 2005 Dec. 13

  18. GWDAW10@UTB, Texas 2005 Dec. 13

  19. Time scale veto Reduction ratio by veto GWDAW10@UTB, Texas 2005 Dec. 13

  20. Detection efficiency Type I Regular collapse Type II Multiple bounce collapse Type III Rapid collapse Simulation • Monte Carlo simulation of 26signals Injected signals 26 kinds signals A&A393 523 (2002) GWDAW10@UTB, Texas 2005 Dec. 13

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