Quenching factor measurements of neon nuclei in neon gas
Bibliographic record
Abstract
The NEWS-G collaboration uses spherical proportional counters (SPCs) to search for weakly interacting massive particles (WIMPs). In this paper, we report the first measurements of the nuclear quenching factor in neon gas at 2 bar using an SPC deployed in a neutron beam at the TUNL facility. The energy-dependence of the nuclear quenching factor is modeled using a simple power law: $\ensuremath{\alpha}{\mathrm{E}}_{\mathrm{nr}}^{\ensuremath{\beta}}$; we determine its parameters by simultaneously fitting the data collected with the detector over a range of energies. We measured the following parameters in $\mathrm{Ne}:{\mathrm{CH}}_{4}$ at 2 bar: $\ensuremath{\alpha}=0.2801\ifmmode\pm\else\textpm\fi{}0.0050$ (fit) $\ifmmode\pm\else\textpm\fi{}0.0045$ (sys) and $\ensuremath{\beta}=0.0867\ifmmode\pm\else\textpm\fi{}0.020$ (fit) $\ifmmode\pm\else\textpm\fi{}0.006$ (sys). Our measurements do not agree with expected values from SRIM or Lindhard theory. We demonstrated the feasibility of performing quenching factor measurements at sub-keV energies in gases using SPCs and a neutron beam.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".