Likelihood smoothing using gravitational wave surrogate models

Abstract

Likelihood surfaces in the parameter space of gravitational wave signals can contain many secondary maxima, which can prevent search algorithms from finding the global peak and correctly mapping the distribution. Traditional schemes to mitigate this problem maintain the number of secondary maxima and thus retain the possibility that the global maximum will remain undiscovered. By contrast, the recently proposed technique of likelihood transform can modify the structure of the likelihood surface to reduce its complexity. We present a practical method to carry out a likelihood transform using a Gaussian smoothing kernel, utilising gravitational wave surrogate models to perform the smoothing operation analytically. We demonstrate the approach with Newtonian and post-Newtonian waveform models for an inspiralling circular compact binary.

14 pages, 15 figures. To match published version

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Cited by in corpus (4)

• Accelerating parameter inference with graphics processing units
• Octupolar invariants for compact binaries on quasi-circular orbits
• An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models
• Tunable compression of template banks for fast gravitational-wave detection and localisation