High-speed reconstruction of long-duration gravitational waves from extreme mass ratio inspirals using sparse dictionary learning

TL;DR

This paper presents a fast sparse dictionary learning method capable of reconstructing year-long gravitational wave signals from extreme mass ratio inspirals within minutes, significantly reducing computational costs for long-duration GW analysis.

Contribution

The study develops and demonstrates a novel SDL algorithm that enables rapid, accurate reconstruction of long-duration EMRI gravitational waveforms, improving efficiency over existing methods.

Findings

Year-long EMRIs reconstructed within 2 minutes

False alarm rate less than 0.001 per year

Mismatch as low as 0.06 in 1.16-day windows

Abstract

Measuring accurate long-duration gravitational waves from extreme mass ratio inspirals (EMRIs) could provide scientifically fruitful knowledge of massive black hole populations and robust tests for general relatively during the LISA mission. However, the immense computational requirements surrounding EMRI data processing and analysis makes their detection and analysis challenging. We further develop and explore a sparse dictionary learning (SDL) algorithm to expeditiously reconstruct EMRI gravitational waveforms lasting as long as 1 year. A suite of year-long EMRI systems are studied to understand the detection and accurate waveform retrieval prospects of the method. We show that full-year EMRIs can be reconstructed within 2 minutes, some with a false alarm rate less than 0.001/yr and with 1.16 day time windows with mismatch as low as 0.06. This provides an encouraging prospect to use the SDL method for long-duration GW searches like that for EMRIs in this study.