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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
gr-qc 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.