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

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.

13 pages, 3 figures, to be published in proceedings of the 12th Gravitational Wave Data Analysis Workshop; v2 has minor changes for consistency with accepted version

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• Importance of including small body spin effects in the modelling of extreme and intermediate mass-ratio inspirals
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• Classifying LISA gravitational wave burst signals using Bayesian evidence
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• Mission Design for the TAIJI misson and Structure Formation in Early Universe
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• Markov chain Monte Carlo searches for Galactic binaries in Mock LISA Data Challenge 1B data sets
• Systematic errors in fast relativistic waveforms for Extreme Mass Ratio Inspirals