Time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data

TL;DR

This paper demonstrates a time-frequency analysis method using the Hierarchical Algorithm for Clusters and Ridges to detect and estimate parameters of EMRI gravitational wave signals in simulated LISA data, addressing challenges of long-duration signals.

Contribution

It introduces a novel application of time-frequency analysis with a hierarchical clustering algorithm for EMRI detection in mock LISA data, improving signal identification.

Findings

Successful detection of EMRI tracks in MLDC data

Effective parameter estimation from identified tracks

Promising results for future LISA data analysis

Abstract

Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from apprxomiately one hundred EMRIs per year, but the data analysis of EMRI signals poses a unique set of challenges due to their long duration and the extensive parameter space of possible signals. One possible approach is to carry out a search for EMRI tracks in the time-frequency domain. We have applied a time-frequency search to the data from the Mock LISA Data Challenge (MLDC) with promising results. Our analysis used the Hierarchical Algorithm for Clusters and Ridges to identify tracks in the time-frequency spectrogram corresponding to EMRI sources. We then estimated the EMRI source parameters from these tracks. In these proceedings, we discuss the results of this analysis of the MLDC round 1.3 data.