2025/05/05 by Jules Perret, M. Arène, Perret, Jules +5 · 1 voice · 2 citations
Engineering · Medicine · Physics and Astronomy · #Advanced X-ray and CT Imaging #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Medical Imaging Techniques and Applications #Nuclear Physics and Applications #astro-ph.IM #gr-qc
paper · pdf · doi:10.48550/arxiv.2505.02589
openalex publication_date 2025/05/05 · arxiv published 2025/05/05 · arxiv updated 2025/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 ∼25 seconds/SIS.