Modeling the Detection of Delayed Neutron Signatures in MCNP6 and Comparisons with Measurements of<sup>233</sup>U,<sup>235</sup>U, and<sup>239</sup>Pu
Bibliographic record
Abstract
Study of the magnitude and temporal behavior of delayed neutrons (DNs) enables the identification of fissile isotopes and a determination of their relative quantities. Thus, the ability to model accurately these neutrons and the methods of their detection is of relevance to nuclear forensics and counterterrorism. The capability of MCNP6 to model these emissions was examined and compared to measurements of the DNs produced by 233U, 235U, and 239Pu after neutron-induced fission. Fissile samples were irradiated in a SLOWPOKE-2 research reactor for 60 s and were then conveyed via pneumatic tubing to an array of six 3He detectors embedded in a paraffin moderator. Several MCNP6 input files were created to reproduce irradiation conditions, temporal DN emission, and the detection arrangement. Nuclear reactions and other effects within the 3He detectors were reproduced by MCNP6, and detection efficiencies of this modeled arrangement determined by MCNP6 were in agreement with experimental measurements. Finally, the library and model DN emission options in the MCNP6v1 release were evaluated and compared to the measured magnitudes and temporal behavior of 233U, 235U, and 239Pu. Significant discrepancies observed between the DN model option and measurements for count times >100 s are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".