Optimization of Processing Parameters and Development of a Radon Trapping System for the NEWS-G Dark Matter Detector
Bibliographic record
Abstract
The direct detection for a dark matter particle is reaching increasingly lower sensitivities and the New Experiments With Spheres-Gas (NEWS-G) collaboration is one of the experiments at the forefront of this. Currently being installed at SNOLAB two kilometres underground, the Spherical Proportional Counter (SPC) used by NEWS-G is designed to detect Weakly Interacting Massive Particles (WIMPs) with a mass less than 1 GeV/c², with a corresponding cross-section of $10¯⁴¹$ cm². A calibration run using pure methane was commissioned at the Laboratoire Souterrain de Modane (LSM) using argon-37, a radioactive gas with two well-characterized decay energies (270 eV and 2.8 keV). The signals from these decay events within the volume of the detector can then be analyzed to accurately map the energy of an event to the detector response. In the presented work, I optimized several processing parameters to ensure the characteristic values from the signals are accurate. In addition, I designed and constructed a radon trapping system in the Piro Lab at the University of Alberta to find the optimal conditions and material to trap radon from detector gases for the NEWS-G experiment at SNOLAB. A closed-loop circulation system was constructed to test two radon trapping materials: Carboxen® 1000 (Sigma Aldrich) and silver zeolite produced by Extraordinary Adsorbents, a company based in Edmonton, Canada. The radon adsorption in nitrogen, argon, and an argon-methane mixture at ambient and dry ice temperatures was then measured, and the performance of each material with different combinations of the above parameters was determined and compared.
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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 | 0.000 |
| 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) | 0.000 | 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".