4D Gamma Spectra Simulation in Geant4 of a Flowing Molten Salt Fuel for Safeguards Applications
Bibliographic record
Abstract
A dynamic Monte Carlo simulation has been developed and implemented in the Geant4 toolkit to model a molten salt fuel flowing through a pipe. We aimed to investigate whether it is possible to detect a change in the enrichment of uranium mixed in the molten salt flowing through a pipe by using gamma spectroscopy. This targets nuclear safeguards and non-proliferation applications, as it would help to apprehend any nuclear diversion activity in a non-invasive manner. The dynamic model includes a uranium molten salt fuel with variable enrichment flowing inside a stainless-steel pipe and surrounded by several high-purity germanium detectors. An automation algorithm was developed using Python to perform dynamic simulations that calculated the motion of the fuel and introduced the time domain in several runs. The product is a ready-to-use tool that takes user inputs based on the desired parameters to simulate and produce the gamma spectrum. Further analysis tools developed are used to extract the desired data and perform the calculations needed to finally create a plot of the fuel's enrichment as a function of time. This paper presents an application of 4D Monte Carlo simulation for the detection of nuclear proliferation activities involving flowing molten salt fuel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".