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

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 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mn>0.001</a:mn><a:mtext> </a:mtext><a:mtext> </a:mtext><a:msup><a:mi>yr</a:mi><a:mrow><a:mo>−</a:mo><a:mn>1</a:mn></a:mrow></a:msup></a:math> 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 gravitational wave searches like that for EMRIs in this study. Published by the American Physical Society 2024