Optimization of a Neutron Long Counter Design by Monte Carlo Simulation
Bibliographic record
Abstract
In a search to optimize neutron long counter design for overall efficiency and flat energy response, Monte Carlo simulations were carried out for a variety of detector design parameters using the Monte Carlo N-Particle Extended code. Based on the standard long counter design by McTaggart, moderator diameter, moderator back length, and longitudinal hole diameter were sequentially varied, and the sensitivity of each parameter to the long counter response was systematically analyzed. For each design, simulations were done in the neutron energy range of 1 keV to 10 MeV. From the simulation results, it turned out that out of the three moderator parameters, the moderator diameter is most sensitive for optimizing the long counter response. As the last design parameter, the effect of the central slow-neutron counter was investigated, which showed a significant difference in the response. The investigation of each design parameter gave clear insight on its effect on the long counter response and enabled one to determine the optimum condition.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".