Bibliographic record
Abstract
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/c2 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 · 1037 cm2 for dark matter candidates of 0.5 GeV/c2 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 multianode read-out that allow high gain and high pressure operation; b) gas purification techniques to remove contaminants (H2O, O2); c) reduction of 210Pb 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.005 |
| 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 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".