Modelling Sensor Performances Across Applied Voltages in the NEWS-G Experiment
Bibliographic record
Abstract
NEWS-G ( New Experiments With Spheres - Gas ) is a dark matter direct detection experiment at SNOLAB. The experiment uses a multi-anode sensor to attract ionization electrons created from interactions within the gas filled detector. When close to the sensor, the electrons trigger an avalanche effect, causing an amplified signal which is recorded by the experiment and analysed. By applying varying voltages to the supporting structure of the sensor, the electric field within the detector can be increased, changing the electron drift. I model proposed sensor geometries, creating a scaled version of the detector currently in the SNOLAB experiment. By placing various voltages on the sensor structures, I investigate the resultant electric field as well as the percentage of signal lost from the anodes as the electric field changes. I have modelled and presented the impact of an altered electric potential placed on the sensor and the impact of various sensor size adjustments. My work will allow NEWS-G to investigate novel sensor geometries within the detector.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".