Use of the MULTINEST algorithm for gravitational wave data analysis

Classical and Quantum Gravity (Impact Factor: 3.17). 04/2009; 26(21). DOI: 10.1088/0264-9381/26/21/215003
Source: arXiv


We describe an application of the MultiNest algorithm to gravitational wave data analysis. MultiNest is a multimodal nested sampling algorithm designed to efficiently evaluate the Bayesian evidence and return posterior probability densities for likelihood surfaces containing multiple secondary modes. The algorithm employs a set of live points which are updated by partitioning the set into multiple overlapping ellipsoids and sampling uniformly from within them. This set of live points climbs up the likelihood surface through nested iso-likelihood contours and the evidence and posterior distributions can be recovered from the point set evolution. The algorithm is model-independent in the sense that the specific problem being tackled enters only through the likelihood computation, and does not change how the live point set is updated. In this paper, we consider the use of the algorithm for gravitational wave data analysis by searching a simulated LISA data set containing two non-spinning supermassive black hole binary signals. The algorithm is able to rapidly identify all the modes of the solution and recover the true parameters of the sources to high precision. Comment: 18 pages, 4 figures, submitted to Class. Quantum Grav; v2 includes various changes in light of referee's comments

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    • "Some methods focus on making individual inner products computationally cheaper: this may be achieved across regions of parameter space through direct interpolation [7] [8], or more generally by using a reduced order quadrature [9] [10]. Other methods seek to reduce the number of required inner products: either by accelerating the convergence to correlation maxima in * Electronic address: † Electronic address: a stochastic search [11] [12] [13], or through compressed-basis decomposition of the template bank in a grid search [14– 16]. "
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