2025/11/01 by Leo Thomas, Thomas, Leo, Miriam Schwarze +3
Engineering · Medicine · #FOS: Physical sciences #Medical Physics (physics.med-ph) #Molecular Communication and Nanonetworks #Nanopore and Nanochannel Transport Studies #Radiation Therapy and Dosimetry
paper · pdf · doi:10.48550/arxiv.2511.00563
openalex publication_date 2025/11/01 · openalex created_date 2025/11/05 · openalex updated_date 2026/07/28
Objective: This work presents a data-driven importance sampling-based variance reduction (VR) scheme inspired by active learning. The method is applied to the estimation of an optimal impact-parameter distribution in the calculation of ionization clusters around a gold nanoparticle (NP). Here, such an optimal importance distribution can not be inferred from principle. Approach: An iterative optimization procedure is set up that uses a Gaussian Process Sampler to propose optimal sampling distributions based on a loss function. The loss is constructed based on appropriate heuristics. The optimization code obtains estimates of the number of ionization clusters in shells around the NP by interfacing with a Geant4 simulation via a dedicated Transmission Control Protocol (TCP) interface. Main results: It is shown that the so-derived impact-parameter distribution easily outperforms the actual, uniform irradiation case. The results resemble those obtained with other VR schemes but do still slightly overestimate background contributions. Significance: While the method presented is a proof-of-principle, it provides a novel method of estimating importance distributions in ill-posed scenarios. The presented TCP interface described here is a simple and efficient method to expose compiled Geant4 code to other scripts, written for example, in Python.