Detection of gamma irradiation with milligray resolution using a slow-light fiber Bragg grating
Bibliographic record
Abstract
For many medical and safety applications, it is important to develop fiber sensors that can detect very low doses of gamma radiation (mGy) with integration times of 1 s or shorter. Here, we describe a sensor based on a new calorimetric technique that we believe is one of the most sensitive and compact reported to date. The fiber subjected to irradiation has a silica core doped with Ce-doped lutetium aluminum garnet nanocrystals selected to achieve a strong radiation-induced absorption (RIA). Light launched in the irradiated fiber is absorbed by RIA, the fiber heats up, and the temperature change is measured with a slow-light fiber Bragg grating (FBG) placed in physical contact with it. Thanks to the doped fiber’s large RIA, and the excellent resolution (mK/√Hz) and low drift (a few mK/min) of the slow-light sensor, with 1.2 W of excitation power at 1040 nm, this sensor has a very low detection limit of ∼6 mGy/√Hz. Thanks to the use of a short FBG (7 mm), it is also extremely small. With straightforward improvements, the detection limit can be reduced to sub-mGy/√Hz. For in situ measurements, this technique can also be easily extended to use the slow-light FBG itself as the radiation sensor.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".