A one-stop function for gravitational-wave detection, identification and inference

TL;DR

This paper introduces a novel function for gravitational-wave data analysis that unifies detection, identification, and inference, especially addressing challenges posed by extreme-mass-ratio inspiral signals.

Contribution

It proposes a new function on the signal space that serves as a statistic, objective function, and likelihood, improving analysis of complex gravitational-wave signals.

Findings

Demonstrates utility for extreme-mass-ratio inspiral signals

Addresses non-local parameter degeneracy issues

Provides a unified framework for detection and inference

Abstract

I define here a novel function on a modeled space of gravitational-wave signals, before studying its properties as a statistic for detection, as an objective function for identification, and as an effective likelihood function for inference. The main motivation behind this work is the open data-analysis problem for signals from extreme-mass-ratio inspirals, which is severely hindered by the presence of strong non-local parameter degeneracy in the signal space. I demonstrate the utility of the proposed function for the analysis of such signals, and suggest various possible directions for future research.