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

TL;DR

This paper enhances time-frequency analysis methods for detecting and characterizing EMRI gravitational wave signals in mock LISA data, introducing new algorithms and estimation techniques to improve detection accuracy.

Contribution

It presents three novel techniques for EMRI signal analysis: a chirp-based track search, inclination estimation, and a Monte Carlo parameter fitting method.

Findings

Successful detection of EMRI tracks in mock data

Improved parameter estimation accuracy

Enhanced robustness of time-frequency analysis methods

Abstract

The planned Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from ~100 extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact objects into massive black holes. The long duration and large parameter space of EMRI signals makes data analysis for these signals a challenging problem. One approach to EMRI data analysis is to use time-frequency methods. This consists of two steps: (i) searching for tracks from EMRI sources in a time-frequency spectrogram, and (ii) extracting parameter estimates from the tracks. In this paper we discuss the results of applying these techniques to the latest round of the Mock LISA Data Challenge, Round 1B. This analysis included three new techniques not used in previous analyses: (i) a new Chirp-based Algorithm for Track Search for track detection; (ii) estimation of the inclination of the source to the line of sight; (iii) a Metropolis-Hastings Monte Carlo over the parameter space in order to find the best fit to the tracks.