Loosely coherent searches for sets of well-modeled signals

TL;DR

This paper presents a high-performance, loosely coherent search method for well-modeled signals, significantly increasing analysis speed and sensitivity in gravitational wave searches through efficient implementation and simulations.

Contribution

The paper introduces a novel loosely coherent statistic implementation that enhances computational efficiency and sensitivity in gravitational wave data analysis.

Findings

Over an order of magnitude speedup in analysis time

Improved sensitivity in continuous gravitational wave searches

Successful validation through Gaussian noise simulations

Abstract

We introduce a high-performance implementation of a loosely coherent statistic sensitive to signals spanning a finite-dimensional manifold in parameter space. Results from full scale simulations on Gaussian noise are discussed, as well as implications for future searches for continuous gravitational waves. We demonstrate an improvement of more than an order of magnitude in analysis speed over previously available algorithms. As searches for continuous gravitational waves are computationally limited, the large speedup results in gain in sensitivity.