Qualification and Calibration of Single-Mode Phosphosilicate Optical Fiber for Dosimetry at CERN
Bibliographic record
Abstract
We report the results of several complementary radiation tests to qualify and calibrate a highly radiation sensitive Single-Mode (SM) Optical Fiber (OF) for distributed dosimetry application at CERN. The Radiation Induced Attenuation (RIA) is the physical phenomenon used to evaluate the Total Ionizing Dose (TID) received by the OF sensor. The radiation response tests comprise online RIA in the Near InfraRed (NIR) domain under60Co γ-rays, X-rays and proton irradiations, as well as post mortem Optical Attenuation (OA) measurement to evaluate long term fading effects of the RIA. In this paper, we focus on the effect of successive irradiations, dose rate dependence and effects related to the change of the irradiation temperature up to 45 °C. On the basis of our investigation, we conclude that the studied OF is a suitable TID sensor and that it is well adapted for distributed OF radiation dose sensing. It is fully compatible with commercially available SM Optical Time Domain Reflectometers, with some noticeable advantages with respect to previously developed systems based on multimode OFs. Finally, the OF we investigated has been selected for the distributed monitoring of low radiation dose levels at CERN in the injector chain (Proton Synchrotron Booster, Proton Synchrotron, Super Proton Synchrotron) and parts of the Large Hadron Collider (LHC).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".