2009/01/31 by Benjamin Aylott, B. E. Aylott, John G. Baker +107 · 140 citations
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Algorithm #Astrophysics #Binary black hole #Computer science #General relativity #Gravitational wave #Gravitational-wave observatory #LIGO #Numerical relativity #Physics #Pulsars and Gravitational Waves Research #Quantum mechanics #Seismic Imaging and Inversion Techniques #Seismology and Earthquake Studies #Theoretical physics #Waveform #gr-qc
paper · pdf · doi:10.1088/0264-9381/26/16/165008
published in Classical and Quantum Gravity 26(16), 165008 (IOP Publishing) · 56 pages, 25 figures; various clarifications; accepted to CQG
arxiv created 2009/07/09 · openalex publication_date 2009/08/03 · arxiv updated 2014/11/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.