Fast Neutron Detection With ${\rm Cs}_{2} {\rm LiYCl}_{6}$
Bibliographic record
Abstract
This paper discusses our initial investigation of fast neutron detection with a Cs2LiYCl6(CLYC) scintillator. CLYC has been already developed for dual mode detectors (thermal neutrons and gamma rays). Described are results collected under mono-energetic irradiation from a Van de Graaff generator and a continuous irradiation from a252Cf source. There are two reactions in which fast neutrons are captured35Cl(n,p)35S and6Li(n,t)α; both have been observed. They produce a proton and a pair of α/t particles, respectively. The response to mono-energetic fast neutrons due to the35Cl(n,p) reaction produces a peak and due to the6Li(n,t) reaction a continuum in the energy spectra. The relation between the peak (continuum) position and the excitation energy is linear within evaluated energy range. This allows for fast neutron spectroscopy. The α/β ratios for both reactions were found to be different and somewhat dependent on the excitation energy. The decay times under the proton excitation slightly differ from these under the α/t excitation, while both are significantly different from the gamma ray excited curves. This is important for pulse shape discrimination. Using continuous excitation from252Cf initial efficiency was estimated for the35Cl(n,p) reaction. For a 1-inch right cylinder crystal the measured intrinsic efficiency is at least 0.06%, while calculated is 0.5%. The discrepancy is due to experimental inaccuracies. The relative detection efficiency scales linearly with the volume. A convolution of the theoretical252Cf fast neutron distribution and the35Cl(n,p) cross-section curve was found to match the experimental data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".