Un-modeled search for black hole binary systems in the NINJA project

TL;DR

This paper evaluates the effectiveness of a minimal-assumption burst detection algorithm, Q-pipeline, in identifying simulated black hole binary signals within the NINJA project data, comparing it to traditional matched filtering methods.

Contribution

It demonstrates the application of a waveform-agnostic burst detection algorithm to simulated black hole merger signals, highlighting its potential in gravitational wave data analysis.

Findings

Q-pipeline detected signals with performance similar to matched filtering.

The study shows promise for burst algorithms in un-modeled gravitational wave searches.

Results are preliminary due to limited simulation statistics.

Abstract

The gravitational wave signature from binary black hole coalescences is an important target for LIGO and VIRGO. The Numerical INJection Analysis (NINJA) project brought together the numerical relativity and gravitational wave data analysis communities, with the goal to optimize the detectability of these events. In its first instantiation, the NINJA project produced a simulated data set with numerical waveforms from binary black hole coalescences of various morphologies (spin, mass ratio, initial conditions), superimposed to Gaussian colored noise at the design sensitivity for initial LIGO and VIRGO. We analyzed this simulated data set with the Q-pipeline burst algorithm. This code, designed for the all-sky detection of gravitational wave bursts with minimal assumptions on the shape of the waveform, filters the data with a bank of sine-Gaussians, or sinusoids with Gaussian envelope. The algorithm's performance was compared to matched filtering with ring-down templates. The results are qualitatively consistent; however due to the low simulation statistics in the first NINJA project, it is premature to draw quantitative conclusions at this stage.