Authors: N. J. Cornish
Date: 21 Apr 2008
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. 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 science driver for the LISA observatory. 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 optimal matched-filter search for these signals are astronomically large, and far out of reach of current computational resources. Here a sub-optimal, hierarchical approach to the EMRI detection problem is developed that employs a directed-stochastic search technique. The algorithm, dubbed Metropolis Hastings Monte Carlo (MHMC), is a close cousin of Markov Chain Monte Carlo and genetic algorithms. The utility of the MHMC approach is demonstrated using simulated data sets from the Mock LISA Data Challenge.
© M. Vallisneri 2012 — last modified on 2010/01/29
Tantum in modicis, quantum in maximis