Precision laser-based measurements of the single electron response of spherical proportional counters for the NEWS-G light dark matter search experiment
Bibliographic record
Abstract
Spherical proportional counters (SPCs) are a novel gaseous detector technology employed by the NEWS-G low-mass dark matter search experiment for their high sensitivity to single electrons from ionization. In this paper, we report on the first characterization of the single electron response of SPCs with unprecedented precision, using a UV-laser calibration system. The experimental approach and analysis methodology are presented along with various direct applications for the upcoming next phase of the experiment at SNOLAB. These include the continuous monitoring of the detector response and electron drift properties during dark matter search runs, as well as the experimental measurement of the trigger threshold efficiency. We measure a mean ionization energy of $\mathrm{W}=27.6\ifmmode\pm\else\textpm\fi{}0.2\text{ }\text{ }\mathrm{eV}$ in $\mathrm{Ne}+{\mathrm{CH}}_{4}$ (2%) for 2.8 keV x-rays, and demonstrate the feasibility of performing similar precision measurements at sub-keV energies for future gas mixtures to be used for dark matter searches at SNOLAB.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".