Driving unmodelled gravitational-wave transient searches using astrophysical information

TL;DR

This paper introduces a hybrid gravitational-wave search method that incorporates astrophysical information into unmodelled searches, improving detection reach by 7-8% for black-hole binaries.

Contribution

It proposes a novel graph-based pattern matching approach that constrains unmodelled searches with astrophysical waveform models without requiring precise phase predictions.

Findings

Increased detection distance reach by 7-8% for black-hole binaries.

Efficient graph-based optimization improves unmodelled search sensitivity.

Method bridges the gap between modelled and unmodelled gravitational-wave searches.

Abstract

Transient gravitational-wave searches can be divided into two main families of approaches: modelled and unmodelled searches, based on matched filtering techniques and time-frequency excess power identification respectively. The former, mostly applied in the context of compact binary searches, relies on the precise knowledge of the expected gravitational-wave phase evolution. This information is not always available at the required accuracy for all plausible astrophysical scenarios, e.g., in presence of orbital precession, or eccentricity. The other search approach imposes little priors on the targetted signal. We propose an intermediate route based on a modification of unmodelled search methods in which time-frequency pattern matching is constrained by astrophysical waveform models (but not requiring accurate prediction for the waveform phase evolution). The set of astrophysically motivated patterns is conveniently encapsulated in a graph, that encodes the time-frequency pixels and their co-occurrence. This allows the use of efficient graph-based optimization techniques to perform the pattern search in the data. We show in the example of black-hole binary searches that such an approach leads to an averaged increase in the distance reach (+7-8\%) for this specific source over standard unmodelled searches.