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

TL;DR

The NINJA project evaluates the effectiveness of various gravitational-wave search algorithms using numerically simulated signals from binary black hole mergers injected into detector-like data, fostering collaboration between numerical relativity and data analysis communities.

Contribution

This study presents the first NINJA analysis, demonstrating the application of multiple search algorithms to numerical relativity waveforms in a simulated detector environment, and compares their detection efficiencies.

Findings

Matched filter algorithms showed high detection efficiency.

Un-modelled burst searches contributed to detection of complex waveforms.

Bayesian methods provided detailed parameter estimates.

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.