arXiv subjects

David Breitenmoser

Publications and source records attributed to David Breitenmoser.

8 recordsLinked to original sources

arXiv2026

Photonuclear Neutron Production in OpenMC: Verification Against MCNPX, FLUKA, and a First-Collision Analytical Solution

The modeling of photonuclear reactions is increasingly important for applications involving high-energy photon fields, including accelerator-driven neutron sources, radiation shielding, medical physics, and fusion technologies. Although the Monte Carlo code OpenMC provides well-verified photoatomic transport capabilities, its photonuclear physics is currently available only in an unofficial development branch and requires independent verification before broader scientific use or possible integration into the official code distribution. This work presents a systematic verification of the OpenMC photonuclear implementation using six single-collision broomstick benchmarks based on 2H, 9Be, and 238U targets irradiated by monoenergetic 5 MeV and 15 MeV photons and by a continuous 1--20 MeV linear accelerator (LINAC)-representative spectrum. OpenMC was compared with MCNPX using common ENDF7u photonuclear data, with FLUKA using its native photonuclear models, and with a first-collision analytical solution for the integrated neutron yield. Calculations using the IAEA/PD-2019 library were also performed to quantify nuclear-data sensitivity. OpenMC and MCNPX agreed within 0.7% in integrated neutron yield for all benchmark cases when the same ENDF7u data were used. FLUKA, which relies on its own native photonuclear models rather than ENDF7u, differed from the analytical solution by approximately 6-9% for the monoenergetic cases and by no more than approximately 4% for the continuous-source cases. Changing the OpenMC library to IAEA/PD-2019 produced deviations of up to 11.3% from the ENDF7u-based analytical solution, with the sensitivity varying strongly by nuclide and source spectrum.

physics.app-ph↗

arXiv2026

Bayesian evidence adaptive pursuit to identify neutron sources with scatter-based spectrometers

Reliable neutron source identification is essential for nuclear nonproliferation, safeguards, and homeland security, but remains challenging because neutron spectral inversion is often ill-conditioned, especially for mixed-source fields with overlapping spectral signatures. Here, we present a scalable Bayesian framework for neutron source identification from recoil spectroscopy measurements using evidence-based model selection. The method introduces a Bayesian Evidence Adaptive Pursuit (BEAP) algorithm that efficiently searches the combinatorial space of candidate source ensembles by iteratively ranking, retaining, and pruning source mixtures according to their Bayesian evidence. We validate the framework experimentally with controlled Cf-252 and deuterium--deuterium neutron-generator measurements, complemented by high-fidelity Monte Carlo simulations spanning representative fission, $(\alpha,\text{n})$, and fusion sources with varying emission rates and mixture complexities. BEAP correctly identifies single- and multi-source mixtures with decisive statistical support ($>\!4\sigma$), requiring between $\mathcal{O}(10^1)$ and $\mathcal{O}(10^6)$ detected recoil events depending on source-mixture complexity, spectral similarity, and emission-rate imbalance. These findings establish BEAP as a practical, scalable, and robust tool for quantitative source identification in mixed neutron fields, significantly extending the operational capabilities of scatter-based neutron spectrometers in nuclear security and emergency response applications.

physics.ins-det↗

arXiv2025

Quantitative mobile gamma-ray spectrometry through Bayesian inference

Accurate quantitative mapping of gamma-ray emitters is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that such mapping can be achieved by combining mobile gamma-ray spectrometry with high-fidelity Monte Carlo simulations and full-spectrum Bayesian inference. Using 6 s of single-pass mobile spectrometry data benchmarked against independent in-situ and laboratory assays, we demonstrate decisive source mixture identification ($>\!\!5\sigma$), meter-scale source localization, and recovery of source activities with percent-level accuracy. The developed method marks a critical advance in quantitative gamma-ray sensing, enabling improved radiological situational awareness, enhanced terrestrial geophysical and geochemical mapping, as well as more robust constraints on radionuclide abundances on extraterrestrial bodies across the Solar System.

physics.ins-det↗

arXiv2025

Identifying Neutron Sources using Recoil and Time-of-Flight Spectroscopy

Neutron-source identification is central to nuclear physics and its applications, from planetary science to nuclear security, yet direct source discrimination from measured neutron spectra remains fundamentally elusive. Here, we introduce a Bayesian protocol that directly infers source ensembles from measured neutron spectra by combining full-spectrum template matching with probabilistic evidence evaluation. Applying this protocol to recoil and time-of-flight spectroscopy, we recover single- and two-source configurations with strong statistical significance ($>\!\!4\sigma$) at event counts as low as $\sim\!\!10^{3}$. These results demonstrate that neutron spectral signatures can be leveraged for robust source identification, opening a new observational window for both fundamental research and operationally driven applications.

physics.ins-det↗

arXiv2025

Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media

Mobile gamma-ray spectrometry (MGRS) systems are essential for localizing, identifying, and quantifying gamma-ray sources in complex environments. Full-spectrum template matching offers the highest accuracy and sensitivity for these tasks but is limited by the computational cost of generating the required spectral templates. Here, we present a generalized full-spectrum modeling framework for MGRS systems in scattering media, enabling near-real-time template generation through dynamic, anisotropic instrument response functions. Benchmarked against high-fidelity brute-force Monte Carlo simulations, our method yields a computational speedup by a factor of $\mathcal{O}(10^7)$, while achieving comparable accuracy with median spectral deviations below 6%. The methodology presented is platform-agnostic and applicable across marine, terrestrial, and airborne domains, unlocking new capabilities for MGRS in a variety of applications, such as environmental monitoring, geophysical exploration, nuclear safeguards, and radiological emergency response.

physics.ins-det↗

arXiv2025

Development and validation of a high-fidelity full-spectrum Monte Carlo model for the Swiss airborne gamma-ray spectrometry system

Airborne Gamma-Ray Spectrometry (AGRS) is a critical tool for radiological emergency response, enabling the rapid identification and quantification of hazardous terrestrial radionuclides over large areas. However, existing calibration methods are limited to a few gamma-ray sources, excluding most radionuclides released in severe nuclear accidents and nuclear weapon detonations, compromising effective response and risk assessment. Here, we present a high-fidelity Monte Carlo model that overcomes these limitations, offering full-spectrum calibration for any gamma-ray source. Unlike previous approaches, our model integrates a detailed mass model of the aircraft and a calibrated non-proportional scintillation model, enabling accurate event-by-event predictions of the spectrometer's response to arbitrarily complex gamma-ray fields. Validation in near-, mid-, and far-field scenarios demonstrates that the model not only addresses major deficiencies of previous approaches but also achieves the accuracy required to supersede empirical calibration methods. This advancement enables high-fidelity spectral signature generation for any gamma-ray source, reduces calibration time and costs, minimizes reliance on high-intensity sources, and eliminates related radioactive waste. The approach presented here is a critical step toward integrating advanced full-spectrum data reduction methods for AGRS, unlocking new capabilities beyond emergency response, such as atmospheric cosmic-ray flux quantification for geophysics and trace-level airborne radionuclide identification for nuclear security.

physics.ins-det↗

arXiv2024

Towards Monte Carlo based Full Spectrum Modeling of Airborne Gamma-Ray Spectrometry Systems

This monograph presents advancements in Airborne Gamma-Ray Spectrometry (AGRS), a critical tool for emergency response to radiological incidents such as severe nuclear accidents or nuclear weapon detonations. Current AGRS calibration and data evaluation methods struggle to accurately quantify many radioactive materials expected in radiological emergencies, limiting the risk assessment and, hence, the effectiveness of emergency response actions. To address these limitations, this work introduces a full spectrum numerical modeling approach that features three key innovations: high-fidelity Monte Carlo simulations that combine an advanced scintillation physics model with detailed geometric representations of the aircraft and detector system; a surrogate model that replicates the Monte Carlo simulations with significantly reduced computation time; and a data evaluation methodology that leverages the surrogate model within a Bayesian inversion framework, enabling the quantification of arbitrarily complex gamma-ray fields. The methodology presented here, rigorously validated through laboratory and field measurements, achieves not only a significant improvement in accuracy and sensitivity over traditional methods but also substantially expands the operational capabilities of AGRS systems for both emergency response scenarios and geophysical surveys. These innovations lay the groundwork for establishing a new global standard for AGRS, ultimately supporting better-informed protective actions and reducing health risks during radiological emergencies.

physics.ins-det↗

arXiv2023

Emulator-based Bayesian Inference on Non-Proportional Scintillation Models by Compton-Edge Probing

Scintillator detector response modelling has become an essential tool in various research fields such as particle and nuclear physics, astronomy or geophysics. Yet, due to the system complexity and the requirement for accurate electron response measurements, model inference and calibration remains a challenge. Here, we propose Compton edge probing to perform non-proportional scintillation model (NPSM) inference for inorganic scintillators. We use laboratory-based gamma-ray radiation measurements with a NaI(Tl) scintillator to perform Bayesian inference on a NPSM. Further, we apply machine learning to emulate the detector response obtained by Monte Carlo simulations. We show that the proposed methodology successfully constrains the NPSM and hereby quantifies the intrinsic resolution. Moreover, using the trained emulators, we can predict the spectral Compton edge dynamics as a function of the parameterized scintillation mechanisms. The presented framework offers a novel way to infer NPSMs for any inorganic scintillator without the need for additional electron response measurements.

physics.ins-det↗