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We consider the problem of characterisation of burst sources detected withthe Laser Interferometer Space Antenna (LISA) using the multi-modal nestedsampling algorithm, MultiNest. We use MultiNest as a tool to search formodelled bursts from cosmic string cusps, and compute the Bayesian evidenceassociated with the cosmic string model. As an alternative burst model, weconsider sine-Gaussian burst signals, and show how the evidence ratio can beused to choose between these two alternatives. We present results from anapplication of MultiNest to the last round of the Mock LISA Data Challenge, inwhich we were able to successfully detect and characterise all three of thecosmic string burst sources present in the release data set. We also presentresults of independent trials and show that MultiNest can detect cosmic stringsignals with signal-to-noise ratio (SNR) as low as ~7 and sine-Gaussian signalswith SNR as low as ~8. In both cases, we show that the threshold at which thesources become detectable coincides with the SNR at which the evidence ratiobegins to favour the correct model over the alternative.

Classifying LISA gravitational wave burst signals using Bayesian evidence