DeepHMC : a deep-neural-network acclerated Hamiltonian Monte Carlo algorithm for binary neutron star parameter estimation
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Perret, Jules, Aréne, Marc, Porter, Edward K.
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Research Foundation, Ministry of Education, Culture, Sports, Science and Technology, Academia Sinica, Centre National de la Recherche Scientifique, Istituto Nazionale di Fisica Nucleare, Ministry of Science and ICT, South Korea, National Science and Technology Council, Science and Technology Facilities Council, Japan Society for the Promotion of Science
OpenAlex ID: W4415032102
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