Recent advancements of the NEWS-G experiment
Bibliographic record
Abstract
NEWS-G (New Experiments With Spheres-Gas) is an experiment aiming to shine a light on the dark matter conundrum with a novel gaseous detector, the spherical proportional counter. It uses light gases, such as hydrogen, helium, and neon, as targets to expand dark matter searches to the sub-GeV/c$^{2}$ mass region. NEWS-G produced its first results with a 60 cm in diameter detector installed at LSM (France), excluding at 90% C.L. cross-sections above $4.4\cdot{10}^{37}$ cm$^{2}$ for dark matter candidates of 0.5 GeV/c$^{2}$ mass. Currently, a 140 cm in diameter detector is being built at LSM and a commissioning run is underway, prior to its installation at SNOLAB (Canada) at the end of the year. Presented here are developments incorporated in this new detector: a) sensor technologies using resistive materials and multi-anode read-out that allow high gain and high pressure operation; b) gas purification techniques to remove contaminants (H$_{2}$O, O$_{2}$); c) reduction of ${}^{210}$Pb induced background through copper electroforming methods; d) utilisation of UV-lasers for detector calibration, detector response monitoring and estimation of gas related fundamental properties. This next phase of NEWS-G will allow searches for low mass dark matter with unprecedented sensitivity.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".