Detection Strategies for Extreme Mass Ratio Inspirals

TL;DR

This paper introduces a hybrid genetic algorithm and Markov Chain Monte Carlo method to detect and analyze complex gravitational wave signals from Extreme Mass Ratio Inspirals in LISA data, overcoming computational challenges.

Contribution

It proposes a novel hybrid data analysis technique combining genetic algorithms and MCMC to efficiently detect EMRI signals in simulated LISA data.

Findings

Effective at blind extraction of weak EMRI signals

Reduces computational load in likelihood calculations

Demonstrates success in simulated data scenarios

Abstract

The capture of compact stellar remnants by galactic black holes provides a unique laboratory for exploring the near horizon geometry of the Kerr spacetime, or possible departures from general relativity if the central cores prove not to be black holes. The gravitational radiation produced by these Extreme Mass Ratio Inspirals (EMRIs) encodes a detailed map of the black hole geometry, and the detection and characterization of these signals is a major scientific goal for the LISA mission. The waveforms produced are very complex, and the signals need to be coherently tracked for hundreds to thousands of cycles to produce a detection, making EMRI signals one of the most challenging data analysis problems in all of gravitational wave astronomy. Estimates for the number of templates required to perform an exhaustive grid-based matched-filter search for these signals are astronomically large, and far out of reach of current computational resources. Here I describe an alternative approach that employs a hybrid between Genetic Algorithms and Markov Chain Monte Carlo techniques, along with several time saving techniques for computing the likelihood function. This approach has proven effective at the blind extraction of relatively weak EMRI signals from simulated LISA data sets.