Gravitational wave detection using multiscale chirplets

TL;DR

This paper introduces a multiscale chirplet-based method for detecting gravitational wave signals, specifically binary black hole coalescences, in noisy LIGO data using dynamic programming for efficient approximation.

Contribution

It presents a novel approach to approximate chirp signals with multiscale chirplets and a polynomial-time detection algorithm for gravitational waves in colored noise.

Findings

Effective detection of binary black hole signals in simulated LIGO noise.

Polynomial-time algorithm for approximating chirp signals.

Potential for improved gravitational wave detection sensitivity.

Abstract

A generic `chirp' of the form h(t) = A(t) cos(phi(t)) can be closely approximated by a connected set of multiscale chirplets with quadratically-evolving phase. The problem of finding the best approximation to a given signal using chirplets can be reduced to that of finding the path of minimum cost in a weighted, directed graph, and can be solved in polynomial time via dynamic programming. For a signal embedded in noise we apply constraints on the path length to obtain a statistic for detection of chirping signals in coloured noise. In this paper we present some results from using this test to detect binary black hole coalescences in simulated LIGO noise.