A Liquid Argon Scintillation Detector for Nuclear Safety and Security Applications
Bibliographic record
Abstract
Noble liquid scintillators, and liquid argon in particular,present a potentially disruptive alternative to conventionalradiation detectors, offering several distinct advantages. One of its most valuable features is the ability to efficiently distinguish between hadronic (neutron) and electromagnetic (gamma) interactions using pulse-shape discrimination (PSD) of the scintillation light. This capability makes these type of detectors particularly well-suited for identifying complex, mixedfield radiation sources, including special nuclear materials. Additionally, liquid argon detectors are highly scalable, allowing for the construction of large-volume systems. The combination of broad detection coverage and strong neutron/gamma discrimination positions this technology as a compelling candidate for radiation portal monitors (RPMs) and a wide range of nuclear security applications,including safeguards and non-proliferation. A Liquid Argon Radiation Monitor (ALARM) is a prototype detector developed at Canadian Nuclear Laboratories, targeting nuclear safety and security applications. In this contribution, we present a detailed description of the detector system and its performance in the detection of gamma-rays and fast neutrons. Excellent separation between gamma rays and neutrons from a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${ }^{252} \text{Cf}$</tex> source was obtained, demonstrating the potential of this detector technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".