Characterization of a prototype detector unit for fast neutron imaging and spectrometry
Bibliographic record
Abstract
Neutron scatter camera, having wide field of view and spectral sensitivity, has been recent research efforts regarding passive fast neutron imaging technique. However, limited the sensitivity to special nuclear material (SUM) prevents it from practical applications. Recently reported Single Volume Scatter Camera (SVSC) [1-5] developed by Sandia National Laboratory demonstrated an order of magnitude increase in detection efficiency. We present a new design of single volume neutron scatter camera in order to improve the position resolution of n-p elastic scatters inside detector unit. The camera composes by ten layers of optically segmented plastic scintillator sheet. Each scintillator sheet consists of a plastic scintillator sheet of 10 cm × 10 cm × 1 cm dimensions and two groups (6×2) of wavelength-shifting fibers with orthogonal directions embedded into grooves on two opposite scintillator surfaces. Fiber scintillation signals were read out by silicon photomultipliers (SiPMs). A prototype detector unit has been characterized experimentally. Light output of the unidimensional six fiber channels from one side of the detector unit was calibrated to be 14.45 photoelectrons per MeVee. Position resolution of the detector unit was measured to be 0.35–0.44 times fiber pitch, corresponding to 5.48 mm for proton recoil energy interval of 1.63–2.60 MeV, and 4.60 mm for proton recoil energy interval of 4.82–5.50 MeV. Energy threshold for recoil proton localization was estimated to be 1.18 MeV. The potential performance optimization methods include reducing light signal loss by increasing the fiber diameter and decreasing the number of channels involved in position reconstruction by narrowing the scintillation signal distribution function of the detector unit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".