An excess power statistic for detection of burst sources of gravitational radiation

TL;DR

This paper introduces an excess power detection method for short-duration gravitational wave bursts, demonstrating its optimality, efficiency, and applicability to multiple detectors with Gaussian noise.

Contribution

The paper develops an excess power detection technique for gravitational wave bursts, including Bayesian thresholds, computational schemes, and extensions to detector networks, showing its near-matched filtering efficiency.

Findings

Method is optimal for known duration and frequency band.

Can be implemented efficiently on a single workstation.

Extensible to multiple interferometers with Gaussian noise.

Abstract

We examine the properties of an excess power method to detect gravitational waves in interferometric detector data. This method is designed to detect short-duration (< 0.5 s) burst signals of unknown waveform, such as those from supernovae or black hole mergers. If only the bursts' duration and frequency band are known, the method is an optimal detection strategy in both Bayesian and frequentist senses. It consists of summing the data power over the known time interval and frequency band of the burst. If the detector noise is stationary and Gaussian, this sum is distributed as a chi-squared (non-central chi-squared) deviate in the absence (presence) of a signal. One can use these distributions to compute frequentist detection thresholds for the measured power. We derive the method from Bayesian analyses and show how to compute Bayesian thresholds. More generically, when only upper and/or lower bounds on the bursts duration and frequency band are known, one must search for excess power in all concordant durations and bands. Two search schemes are presented and their computational efficiencies are compared. We find that given reasonable constraints on the effective duration and bandwidth of signals, the excess power search can be performed on a single workstation. Furthermore, the method can be almost as efficient as matched filtering when a large template bank is required. Finally, we derive generalizations of the method to a network of several interferometers under the assumption of Gaussian noise.