Benchmarking Neutron Counting System for Passive Measurements and Active Interrogation of Unknown Objects for Fissile and Fissionable Materials Determination
Bibliographic record
Abstract
The low rate at which some fissile isotopes, such as 235U, 233U, and 239Pu, undergo spontaneous fission leads to a weak signal, resulting in a high-uncertainty in applying passive neutron counting techniques. Stimulating fission through active neutron interrogation can overcome this issue. At Canadian Nuclear Laboratories, a 252Cf and a deuterium-deuterium neutron source are available. In this study, a neutron counting system was designed to perform passive measurements and active neutron interrogation for a search of special nuclear material. The detection system consists of a cylindrical cavity surrounded by a polyethylene moderator with 3He detectors interspersed throughout. When used for passive measurements, the sample is placed in the cylindrical cavity, whereas in active interrogation mode, the 252Cf neutron source and the sample are placed in close proximity to each other in the cylindrical cavity. Measurements that actively interrogated samples, notably containing (among other isotopes) either 235U or 239Pu whose mass was on the order of fractions of a gram, carried out using the 252Cf neutron source found that the average delayed neutron count rate was on the same order of magnitude as those obtained from passive measurements using several kilograms of natural uranium. The Monte Carlo N-Particle 6 version 2.0 radiation transport code was used to simulate the aforementioned active interrogations and to inform the experimental results. Results showed that, due to the close proximity of the polyethylene moderator to the 252Cf source, the neutron energy spectrum traversing the fissile sample has a significant thermal component that maximizes the fission reaction rate in the interrogated fissile samples, thereby allowing for successful measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".