Active learning-based variance reduction for Monte Carlo simulations: A feasibility study for the nanodosimetry around a gold nanoparticle
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917054597038080 |
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| author | Thomas, Leo Schwarze, Miriam Rabus, Hans |
| author_facet | Thomas, Leo Schwarze, Miriam Rabus, Hans |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Active learning-based variance reduction for Monte Carlo simulations: A feasibility study for the nanodosimetry around a gold nanoparticle Thomas, Leo Schwarze, Miriam Rabus, Hans Medical Physics 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. |
| title | Active learning-based variance reduction for Monte Carlo simulations: A feasibility study for the nanodosimetry around a gold nanoparticle |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2511.00563 |