DeepHMC : a deep-neural-network acclerated Hamiltonian Monte Carlo algorithm for binary neutron star parameter estimation

TL;DR

DeepHMC introduces a neural network-accelerated Hamiltonian Monte Carlo method that significantly speeds up binary neutron star parameter estimation, achieving high accuracy and efficiency in a practical timeframe.

Contribution

The paper presents DeepHMC, a novel DNN-accelerated HMC algorithm that drastically reduces gradient computation time for gravitational wave data analysis.

Findings

DNN-based gradients are 30 times faster than binning gradients.

DeepHMC produces accurate results consistent with LVK data.

It acquires 5000 independent samples in hours, not days.

Abstract

We present a deep neural network (DNN) accelerated Hamiltonian Monte Carlo (HMC) algorithm called DeepHMC for the inference of binary neutron star systems. The HMC is a non-random walk sampler that uses background gradient information to accelerate the convergence of the sampler. While faster converging than a random-walk sampler, in theory by a factor of the dimensionality of the problem, a known computational bottleneck for HMC algorithms is the calculation of gradients of the log-likelihood. We demonstrate that Hamiltonian trajectories based on a DNN gradients are 30 times faster than those based on the relative binning gradients, and 7000 times faster than trajectories based on a naive likelihood gradient calculation. Using the publicly available 128 second LVK data set for the binary neutron star mergers GW170817 and GW190425, we show that not only does DeepHMC produce produces highly accurate and consistent results with the LVK public data, but acquires 5000 statistically independent samples (SIS) in the $12D$ parameter space in approximately two hours on a Macbook pro for GW170817, with a cost of $<1$ second/SIS, and 2.5 days for GW190425, with a cost of $\sim25$ seconds/SIS.