A neural-network surrogate for the log-likelihood gradients makes Hamiltonian Monte Carlo trajectories 30 times faster than relative-binning gradients and recovers LVK-consistent posteriors for two binary neutron star mergers.
A Hamiltonian Monte Carlo method for Bayesian Inference of Supermassive Black Hole Binaries
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abstract
We investigate the use of a Hamiltonian Monte Carlo to map out the posterior density function for supermassive black hole binaries. While previous Markov Chain Monte Carlo (MCMC) methods, such as Metropolis-Hastings MCMC, have been successfully employed for a number of different gravitational wave sources, these methods are essentially random walk algorithms. The Hamiltonian Monte Carlo treats the inverse likelihood surface as a "gravitational potential" and by introducing canonical positions and momenta, dynamically evolves the Markov chain by solving Hamilton's equations of motion. We present an implementation of the Hamiltonian Markov Chain that is faster, and more efficient by a factor of approximately the dimension of the parameter space, than the standard MCMC.
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gr-qc 1years
2025 1verdicts
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DeepHMC : a deep-neural-network acclerated Hamiltonian Monte Carlo algorithm for binary neutron star parameter estimation
A neural-network surrogate for the log-likelihood gradients makes Hamiltonian Monte Carlo trajectories 30 times faster than relative-binning gradients and recovers LVK-consistent posteriors for two binary neutron star mergers.