Slide 1

A walk through some statistic details of LSC results Slide 2 CBC (inspiral) papers S1: Analysis of LIGO data for gravitational waves from binary neutron stars. Phys. Rev. D 69 (2004) 122001 gr-qc/0308069 Phys. Rev. D 69 (2004) 122001 gr-qc/0308069 S2: Search for gravitational waves from galactic and extra-galactic binary neutron stars. Phys. Rev. D 72 (2005) 082001 gr-qc/0505041 Phys. Rev. D 72 (2005) 082001 gr-qc/0505041 S3/S4: Search for gravitational waves from binary inspirals in S3 and S4 LIGO dataPhys. Rev. D 77 (2008) 062002 arXiv:0704.3368Phys. Rev. D 77 (2008) 062002arXiv:0704.3368 S5/VSR1: Search for Gravitational Waves from Compact Binary Coalescence in LIGO and Virgo Data from S5 and VSR1 Phys. Rev. D 82 (2010) 102001 arXiv:1005.4655 Also Sensitivity to Gravitational Waves from Compact Binary Coalescences Achieved during LIGO's Fifth and Virgo's First Science Run, arXiv:1003.2481Phys. Rev. D 82 (2010) 102001arXiv:1005.4655arXiv:1003.2481 S6/VSR2, 3: Search for Gravitational Waves from Low Mass Compact Binary Coalescence in LIGO's Sixth Science Run and Virgo's Science Runs 2 and 3 arXiv:1111.7314 P1100034. Also Sensitivity Achieved by the LIGO and Virgo Gravitational Wave Detectors during LIGOs Sixth and Virgos Second and Third Science Runs, arXiv:1203.2674arXiv:1111.7314P1100034arXiv:1203.2674 Slide 3 7/9/2003LIGO Scientific Collaboration - Amaldi 20033 How to detect inspiral waves Use template based matched filtering algorithm Template waveforms for non-spinning binaries 2.0 post-Newtonian approx. D: effective distance; a: phase Discrete set of templates labeled by I=(m1, m2) 1.0 Msun < m1, m2 < 3.0 Msun 2110 templates At most 3% loss in SNR s(t) = (1Mpc/D) x [ sin(a) h I s (t-t0) + cos(a) h I c (t-t0)] Slide 4 7/9/20034 Optimal Filtering Using FFTs Transform data to frequency domain : Calculate template in frequency domain : Combine, weighting by power spectral density of noise, and then inverse Fourier transform gives the filter output at all times: Find maxima of over arrival time and phase Slide 5 7/9/2003LIGO Scientific Collaboration - Amaldi 20035 Chi-Squared Veto Many large glitches in the data can lead to a filter output with large SNR The essence of a chirp is that the signal power is distributed over frequencies in a particular way Divide template into sub-bands (p=8) and calculate 2 : Correct for large signals which fall between points in template bank and apply a threshold cut: Slide 6 Multiple detectors: S2 example Slide 7 Results of a search Candidates and their significance (detections?). Upper limits on rate of coalescences (frequentist or Bayesian). Slide 8 Slide 9 7/9/2003LIGO Scientific Collaboration - Amaldi 20039 S1 Inspiral Search: results Use triggers from H 4km and L 4km interferometers: T = 236 hours Max SNR observed: 15.9 An event seen in L1 only, with effective distance = 95 kpc There are no event candidates in the coincidence category Monte Carlo simulation efficiency for SNR=15.9: = 53% Effective number of Milky Way-equivalent galaxies surveyed: N G = (L pop /L G )=0.53x1.13=0.60 Uncertainties (calibration, etc): Slide 10 7/9/2003LIGO Scientific Collaboration - Amaldi 200310 Inspiral Search: results Limit on binary neutron star coalescence rate: R90% (Milky Way) < = 2.3 x (1/N G ) (1/T) = 140 (0.60/N G ) /yr With N G =0.60-0.10 we derive R< 170 /yr /MWEG Compare with: Previous experimental results: LIGO 40m 94: 0.5/hr (25hrs, D