14-MeV and Atmospheric Neutron Monitoring Through Optical Fiber Dosimeters
Bibliographic record
Abstract
Radiation-induced attenuation (RIA)-based dosimetry exploiting phosphosilicate optical fiber (OF) has been established as a reliable and precise dosimetry technique, to monitor the total ionizing dose (TID) in a variety of harsh environments. We evaluate here the potential of this technology to monitor 14-MeV and atmospheric neutrons. Experiments performed at the TRIUMF Neutron Facility (TNF) from TRIUMF and Frascati neutron generator (FNG) from ENEA demonstrate that after calibration, the fiber dosimeter can serve as an efficient fluence monitor, with the RIA being linearly dependent on the neutron fluence. Using Geant4 simulations, we show that the radiation sensitivity of the fiber, around 3.5 dB$\cdot $km${}^{-1}~\cdot $Gy−1 for 14-MeV neutrons, agrees with the ones found in previous studies for photons and protons. For atmospheric neutrons, the calibration differs by a factor of up to 2.5 with respect to the usual factor. Extensive simulations are performed showing that in these neutron environments, the consideration of the real 3-D structure of the fiber coil is mandatory for a fine calibration of the dosimeter. Perspectives for fiber dosimetry of neutron-rich environments are then 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".