arXiv subjects

Christopher Fichtlscherer

Publications and source records attributed to Christopher Fichtlscherer.

3 recordsLinked to original sources

arXiv2026

PyNMC: An Open-Source Framework for Neutron Multiplicity Counting Simulation Coupling OpenMC, FREYA, and ALPHANSO

Neutron multiplicity counting (NMC) underpins plutonium assay in nuclear safeguards, arms control, and disarmament verification, but existing simulation tools are essentially limited to MCNPX-PoliMi [1] (export-controlled, MCNP license required) and ONMS [2] (open-source but built on Geant4 with no scripting API); other codes (RMC, MCNP-PTA) are institute-internal. We present PyNMC, an open-source, Python-native NMC simulation framework that couples OpenMC for transport with FREYA for event-by-event correlated prompt-neutron emission and ALPHANSO for native ($\alpha$, n)-source estimates, together with collision-level time-tagged event recording and a Python shift-register post-processor cross-validated against ONMS. The framework is validated against the ESARDA Neutron Multiplicity Benchmark on bare $^{252}$Cf (c2-10, c2-100), the low-multiplication Pu metal case c3s (M = 1.12 from an independent k-eigenvalue calculation; ESARDA spec M = 1.08), and a 10 g PuO2 sample with an ($\alpha$, n)-source term (c4s); an internal stress-test extension to a $\approx 100$ g Pu metal sample at M = 1.29 is reported alongside but lies beyond the ESARDA participant range. For c4s, ALPHANSO gives $\alpha$ = 0.78 with modern cross-section data; the reported benchmark comparison rescales the ($\alpha$,n) rate to the ESARDA value $\alpha$ = 0.853. Simulated rates agree with point-model predictions for all cases, and with the published ESARDA participant-code scatter where participant results exist. The framework is shipped as a Docker container under the MIT license and is openly available on GitHub at github.com/cfichtlscherer/nmc.

physics.ins-det↗

arXiv2026

Machine learning inference of fission yields from gamma spectroscopy for very low-yield nuclear test verification

Very low-yield nuclear tests pose a major verification challenge for the zero-yield standard of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The zero-yield standard prohibits any explosive experiment that produces a self-sustaining fission chain reaction while allowing subcritical experiments. Previous research shows that on-site gamma spectroscopy of post-test debris provides useful insight into the criticality level, although it remains heavily dependent on knowledge of certain experimental settings. Here, we adopt a new approach whereby machine learning models are trained on simulated gamma spectroscopy data to infer the fission yield of a nuclear very low-yield test. Using high-fidelity 3D Monte Carlo particle transport simulations, we generated gamma spectra measured outside containment vessels after very low-yield tests for 66 million representative scenarios. From these spectra, we extracted 82 fission-product-to-plutonium-239 peak ratios, then trained ML models for two tasks: (1) binary classification of whether a test exceeded a chosen yield threshold, and (2) regression to estimate the actual yield. We find that XGBoost performs best on the classification task across the most policy-relevant yield range. The classifier achieves high accuracy even for yields near the chosen threshold (e.g., >95% for yields +-100 g around a threshold at 1 kg TNT), and the regressor presents a mean absolute relative error of 12.4% for measurements taken a month to a year after the test. These results demonstrate that using machine learning to infer the yield of a past very low-yield nuclear test from gamma spectroscopy data is feasible and accurate. This approach can support efforts to establish a robust verification protocol for the zero-yield standard and could pave the way for a future yield threshold-based verification regime that is both technically feasible and politically viable.

physics.ins-det↗

arXiv2026

Reconstructions of Single Pixel X-Ray Transforms with Applications in Nuclear-Disarmament Verification

In nuclear arms control and disarmament processes, it is crucial to determine whether an object is a nuclear weapon or not without revealing sensitive information about it. At the MIT: Laboratory for Nuclear Security and Policy, such a nuclear verification method was developed, showcasing a transmission-based approach [1]. This method's essential part rests on a mathematical operation, the Single-Pixel X-Ray Transform: a cone of X-rays transmits an object and the remaining intensity is measured with a single-pixel detector. This transformation and the recovery of objects from dimensionless single-pixel measurements more generally has only been analyzed to a limited extent. In this work, we investigate some of the Single Pixel X-Ray Transform's mathematical properties. More specifically, we show that the Single Pixel X-ray transform is non-linear, continuous, Fr\'echet-differentiable and convex. We also introduce a method of reconstructing an object based only on a finite number of dimensionless, noisy Single Pixel X-Ray Transform measurement values. This method is based on Douglas-Rachford splitting and uses total variation denoising. We present an implementation for this method, focusing on rotational symmetric objects, as they allow the use of a one-dimensional direct total variation denoising algorithm [2].

math.NA↗