Rapidly evaluating the compact binary likelihood function via interpolation

Abstract

Bayesian parameter estimation on gravitational waves from compact binary coalescences (CBCs) typically requires millions of template waveform computations at different values of the parameters describing the binary. Sampling techniques such as Markov chain Monte Carlo and nested sampling evaluate likelihoods, and hence compute template waveforms, serially; thus, the total computational time of the analysis scales linearly with that of template generation. Here we address the issue of rapidly computing the likelihood function of CBC sources with non-spinning components. We show how to efficiently compute the continuous likelihood function on the three-dimensional subspace of parameters on which it has a non-trivial dependence (the chirp mass, symmetric mass ratio and coalescence time) via interpolation. Subsequently, sampling this interpolated likelihood function is a significantly cheaper computational process than directly evaluating the likelihood; we report improvements in computational time of two to three orders of magnitude while keeping likelihoods accurate to < 0.025 %. Generating the interpolant of the likelihood function over a significant portion of the CBC mass space is computationally expensive but highly parallelizable, so the wall time can be very small relative to the time of a full parameter-estimation analysis.

5 pages, 1 figure

References in corpus (5)

• Comparison of post-Newtonian templates for compact binary inspiral signals in gravitational-wave detectors
• Singular value decomposition applied to compact binary coalescence gravitational-wave signals
• Degeneracies in Sky Localisation Determination from a Spinning Coalescing Binary through Gravitational Wave Observations: a Markov-Chain Monte-Carlo Analysis for two Detectors
• Parameter estimation for signals from compact binary inspirals injected into LIGO data
• Fortifying the characterization of binary mergers in LIGO data

Cited by in corpus (9)

• Exploring the Bayesian parameter estimation of binary black holes with LISA
• Accelerating parameter inference with graphics processing units
• Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
• Constraints on the contributions to the observed binary black hole population from individual evolutionary pathways in isolated binary evolution
• Fast likelihood evaluation using meshfree approximations for reconstructing compact binary sources
• An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models
• Localization of binary neutron star mergers with a single Cosmic Explorer
• Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
• Accelerated parameter estimation of supermassive black hole binaries in LISA using a meshfree approximation