Optimization of spherical proportional counter backgrounds and response for low mass dark matter search
Bibliographic record
Abstract
The NEWS-G collaboration uses Spherical Proportional Counters to search for Weakly Interacting Massive Particles (WIMP). The first detector developed for this goal is a 60 cm diameter sphere installed at the Laboratoire Souterrain de Modane in France. In 2015, the collaboration took a run with neon as the target material for an exposure of 9.7 $\mathrm{kg\cdot days}$. This run allowed new limits to be set on the spin-independent WIMP-nucleon cross-sections with $\mathrm{90\%}$ confidence upper limit of $\mathrm{\sigma_{SI} < 4.4 \times 10^{-37} cm^{2}}$ for a $\mathrm{0.5\, GeV/c^{2}}$ WIMP. The study of the background events observed during this run shows that it is dominated by the presence of the $\mathrm{^{210}Pb}$ decay chain in the different materials composing the detector, its shielding, and on the inner surface of the sphere. The experiences acquired during the utilization of SEDINE and the analysis of its data allowed a procedure to be developed to avoid radioactive contaminations and minimize the background of the experiment. The background of the next detector was estimated by a stringent selection of the materials, the measurements of their radioactive contaminations and the simulation of the different components. The development of new sensors allows a better homogeneity of the detector response and good data acquisition in large detector. The new detector is a 140 cm diameter sphere, to be installed at SNOLAB in Canada in 2020. Its performance will be also enhanced by the development of methods of signal characterisation and calibration.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".