Active learning-based variance reduction for Monte Carlo simulations: A feasibility study for the nanodosimetry around a gold nanoparticle

TL;DR

This study introduces a novel, data-driven importance sampling variance reduction method using active learning and Gaussian processes to improve Monte Carlo simulations of ionization around gold nanoparticles, demonstrating promising results.

Contribution

It proposes a new active learning-based variance reduction scheme for Monte Carlo simulations, specifically for estimating impact-parameter distributions in nanodosimetry, which was previously infeasible.

Findings

Impact-parameter distribution outperforms uniform case

Method slightly overestimates background contributions

TCP interface effectively links Geant4 with scripting languages

Abstract

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.