Analysis and identification of alpha events for NEWS-G
Bibliographic record
Abstract
The NEWS-G experiment uses a spherical proportional counter filled with gas in order to detect potential dark matter particles that can ionize the gas after a nuclear recoil. The detector works by attracting the free electrons towards the center of the sphere where there is a high voltage anode inducing a radial electric field. Near the anode, the accelerated electrons then cause a Townsend avalanche that produces many drifting ions, which creates an identifiable electrical signal. Since this method has been most efficient at energies below 1 GeV, that is where keeping the background contamination at minimum is of upmost importance. However, there is approximately 20 mBq of alpha particles coming from impurities in the copper surface of the sphere that create a sudden influx of events close to the region of interest for up to five seconds. These alphas also lead to fluctuations in the electric field, which in turn alters the time taken for electrons to reach the anode at the center. This presentation aims to show how the consequences of alpha particles in the detector can be characterized, as well as how those consequences can be used to better identify alpha events in order to remove the correlated influx of low energy events. *On behalf of the NEWS-G collaboration
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".