A Constrained Metropolis-Hastings Search for EMRIs in the Mock LISA Data Challenge 1B

TL;DR

This paper presents a novel constrained Metropolis-Hastings algorithm for detecting extreme-mass-ratio inspiral sources in simulated LISA data, improving search robustness and parameter recovery.

Contribution

The paper introduces a new Monte-Carlo search method with advanced annealing and harmonic identification to enhance EMRI detection in LISA data.

Findings

Successful detection of EMRI sources in Round 1B data

Algorithm reduces likelihood of false maxima

Parameter recovery has improved over previous methods

Abstract

We describe a search for the extreme-mass-ratio inspiral sources in the Round 1B Mock LISA Data Challenge data sets. The search algorithm is a Monte-Carlo search based on the Metropolis-Hastings algorithm, but also incorporates simulated, thermostated and time annealing, plus a harmonic identification stage designed to reduce the chance of the chain locking onto secondary maxima. In this paper, we focus on describing the algorithm that we have been developing. We give the results of the search of the Round 1B data, although parameter recovery has improved since that deadline. Finally, we describe several modifications to the search pipeline that we are currently investigating for incorporation in future searches.