Use of GEANT4 vs. MCNPX for the characterization of a boron-lined neutron detector
Bibliographic record
Abstract
This work compares GEANT4 with MCNPX in the characterization of a boron-lined neutron detector . The neutron energy ranges simulated in this work (0.025 eV to 20 MeV) are the traditional domain of MCNP simulations. This paper addresses the question, how well can GEANT4 and MCNPX be employed for detailed thermal neutron detector characterization? To answer this, GEANT4 and MCNPX have been employed to simulate detector response to a 252 Cf energy spectrum point source , as well as to simulate mono-energetic parallel beam source geometries. The 252 Cf energy spectrum simulation results demonstrate agreement in detector count rate within 3% between the two packages, with the MCNPX results being generally closer to experiment than are those from GEANT4. The mono-energetic source simulations demonstrate agreement in detector response within 5% between the two packages for all neutron energies, and within 1% for neutron energies between 100 eV and 5 MeV. Cross-checks between the two types of simulations using ISO-8529 252 Cf energy bins demonstrates that MCNPX results are more self-consistent than are GEANT4 results, by 3–4%.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".