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arXiv · 0901.4399

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

Benjamin Aylott·John G. Baker·William D. Boggs·Michael Boyle·Patrick R. Brady·Duncan A. Brown·Bernd Brügmann·Luisa T. Buchman·Alessandra Buonanno·Laura Cadonati·Jordan Camp·Manuela Campanelli·Joan Centrella·Shourov Chatterji·Nelson Christensen·Tony Chu·Peter Diener·Nils Dorband·Zachariah B. Etienne·Joshua Faber·Stephen Fairhurst·Benjamin Farr·Sebastian Fischetti·Gianluca Guidi·Lisa M. Goggin·Mark Hannam·Frank Herrmann·Ian Hinder·Sascha Husa·Vicky Kalogera·Drew Keppel·Lawrence E. Kidder·Bernard J. Kelly·Badri Krishnan·Pablo Laguna·Carlos O. Lousto·Ilya Mandel·Pedro Marronetti·Richard Matzner·Sean T. McWilliams·Keith D. Matthews·R. Adam Mercer·Satyanarayan R. P. Mohapatra·Abdul H. Mroué·Hiroyuki Nakano·Evan Ochsner·Yi Pan·Larne Pekowsky·Harald P. Pfeiffer·Denis Pollney·Frans Pretorius·Vivien Raymond·Christian Reisswig·Luciano Rezzolla·Oliver Rinne·Craig Robinson·Christian Röver·Lucía Santamaría·Bangalore Sathyaprakash·Mark A. Scheel·Erik Schnetter·Jennifer Seiler·Stuart L. Shapiro·Deirdre Shoemaker·Ulrich Sperhake·Alexander Stroeer·Riccardo Sturani·Wolfgang Tichy·Yuk Tung Liu·Marc van der Sluys·James R. van Meter·Ruslan Vaulin·Alberto Vecchio·John Veitch·Andrea Viceré·John T. Whelan·Yosef Zlochower
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Abstract

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.

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Benjamin Aylott, John G. Baker, William D. Boggs, Michael Boyle, Patrick R. Brady, Duncan A. Brown, Bernd Brügmann, Luisa T. Buchman, Alessandra Buonanno, Laura Cadonati, Jordan Camp, Manuela Campanelli, Joan Centrella, Shourov Chatterji, Nelson Christensen, Tony Chu, Peter Diener, Nils Dorband, Zachariah B. Etienne, Joshua Faber, Stephen Fairhurst, Benjamin Farr, Sebastian Fischetti, Gianluca Guidi, Lisa M. Goggin, Mark Hannam, Frank Herrmann, Ian Hinder, Sascha Husa, Vicky Kalogera, Drew Keppel, Lawrence E. Kidder, Bernard J. Kelly, Badri Krishnan, Pablo Laguna, Carlos O. Lousto, Ilya Mandel, Pedro Marronetti, Richard Matzner, Sean T. McWilliams, Keith D. Matthews, R. Adam Mercer, Satyanarayan R. P. Mohapatra, Abdul H. Mroué, Hiroyuki Nakano, Evan Ochsner, Yi Pan, Larne Pekowsky, Harald P. Pfeiffer, Denis Pollney, Frans Pretorius, Vivien Raymond, Christian Reisswig, Luciano Rezzolla, Oliver Rinne, Craig Robinson, Christian Röver, Lucía Santamaría, Bangalore Sathyaprakash, Mark A. Scheel, Erik Schnetter, Jennifer Seiler, Stuart L. Shapiro, Deirdre Shoemaker, Ulrich Sperhake, Alexander Stroeer, Riccardo Sturani, Wolfgang Tichy, Yuk Tung Liu, Marc van der Sluys, James R. van Meter, Ruslan Vaulin, Alberto Vecchio, John Veitch, Andrea Viceré, John T. Whelan, Yosef Zlochower. 2009-01-28. Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project. https://doi.org/10.1088/0264-9381/26/16/165008

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