Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach

Author:

Schmitz Benedikt12ORCID,Scheuren Stefan1ORCID

Affiliation:

1. Institut für Kernphysik (IKP), Technische Universität Darmstadt, Schlossgartenstr. 9, 64289 Darmstadt, Germany

2. Elektrotechnik und Informationstechnik (EIT), Hochschule Darmstadt, Birkenweg 8, 64295 Darmstadt, Germany

Abstract

The development of compact neutron sources for applications is extensive and features many approaches. For ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for proton and deuteron beams with arbitrary energy distributions with kinetic energies from 3 MeV to 97 MeV. This model makes it possible to compare different ion-based neutron source concepts against each other quickly. This contribution derives a predictive model using Monte Carlo simulations (an order of 50,000 simulations) and deep neural networks. It is the first time a model of this kind has been developed. With this model, lengthy Monte Carlo simulations, which individually take a long time to complete, can be circumvented. A prediction of neutron spectra then takes some milliseconds, which enables fast optimization and comparison. The models’ shortcomings for low-energy neutrons (<0.1 MeV) and the cut-off prediction uncertainty (±3 MeV) are addressed, and mitigation strategies are proposed.

Funder

HMWK through the LOEWE center “Nuclear Photonics”

Graduate School CE within the Centre for Computational Engineering at Technische Universität Darmstadt

Trumpf GmbH & Co. KG

Publisher

MDPI AG

Reference20 articles.

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3. First steps toward the development of SONATE, a Compact Accelerator driven Neutron Source;Thulliez;EPJ Web Conf.,2020

4. ESFRI Physical Sciences and Engineering Strategy Working Group-NeutronLandscape Group (2015). Neutron Scattering Facilities in Europe—Present Status and Future Perspectives, European Strategy Forum on Research Infrastructures.

5. Conrad, H. (2021). Handbook of Particle Detection and Imaging, Springer International Publishing. Chapter Spallation: Neutrons Beyond Nuclear Fission.

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