Detecting extreme mass ratio inspiral events in LISA data using the Hierarchical Algorithm for Clusters and Ridges (HACR)

TL;DR

This paper evaluates the HACR time-frequency algorithm for detecting extreme mass ratio inspiral events in LISA data, demonstrating its effectiveness in identifying signals at significant distances and discussing its potential for parameter estimation.

Contribution

It introduces and assesses the HACR algorithm's capability to detect EMRI signals in LISA data, including tuning methods and potential for parameter extraction.

Findings

HACR detects EMRI events up to ~2.6 Gpc with binned spectrograms.

HACR outperforms the simple Excess Power method in detection range.

Cluster properties from HACR could aid in parameter estimation.

Abstract

One of the most exciting prospects for the Laser Interferometer Space Antenna (LISA) is the detection of gravitational waves from the inspirals of stellar-mass compact objects into supermassive black holes. Detection of these sources is an extremely challenging computational problem due to the large parameter space and low amplitude of the signals. However, recent work has suggested that the nearest extreme mass ratio inspiral (EMRI) events will be sufficiently loud that they might be detected using computationally cheap, template-free techniques, such as a time-frequency analysis. In this paper, we examine a particular time-frequency algorithm, the Hierarchical Algorithm for Clusters and Ridges (HACR). This algorithm searches for clusters in a power map and uses the properties of those clusters to identify signals in the data. We find that HACR applied to the raw spectrogram performs poorly, but when the data is binned during the construction of the spectrogram, the algorithm can detect typical EMRI events at distances of up to $\sim2.6$Gpc. This is a little further than the simple Excess Power method that has been considered previously. We discuss the HACR algorithm, including tuning for single and multiple sources, and illustrate its performance for detection of typical EMRI events, and other likely LISA sources, such as white dwarf binaries and supermassive black hole mergers. We also discuss how HACR cluster properties could be used for parameter extraction.