Thin gap chamber performance tests under several MeV neutron sources
Bibliographic record
Abstract
Thin gap chamber (TGC) is a very thin multiwire proportional chamber of only a few millimeters. It has a quick response (about 20ns), and its production costs are relatively low. TGCs have been used as large area detectors in high energy physics such as Large Electron-Positron collider (LEP) and will be used in the Large Hadron collider (LHC) experiment. However, the characteristics of TGCs under neutrons are not yet clearly understood. As the energy deposits of several MeV neutrons in TGCs are large, the possible effect of these deposits on the operation of the detector is a concern. We studied TGC performance in relation to efficiency, charge distribution, and operation stability using several gas mixtures (CO2∕n-pentane and CF4∕n-pentane) with 2.5 and 14MeV neutron sources at Fusion Neutronics Source (FNS) in Japan Atomic Energy Agency. Operation stability using a CF4 based gas was more than 100 times greater than with CO2 based gas, while the minimum ionizing particle signal gain was almost the same. The detection efficiencies were around 0.1% (14MeV) and 0.02% (2.5MeV). These results are consistent with our simulation studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".