First Probe of Sub-GeV Dark Matter beyond the Cosmological Expectation with the COHERENT CsI Detector at the SNS
Bibliographic record
Abstract
The COHERENT Collaboration searched for scalar dark matter particles produced at the Spallation Neutron Source with masses between 1 and 220 MeV/c^{2} using a CsI[Na] scintillation detector sensitive to nuclear recoils above 9 keV_{nr}. No evidence for dark matter is found and we thus place limits on allowed parameter space. With this low-threshold detector, we are sensitive to coherent elastic scattering between dark matter and nuclei. The cross section for this process is orders of magnitude higher than for other processes historically used for accelerator-based direct-detection searches so that our small, 14.6 kg detector significantly improves on past constraints. At peak sensitivity, we reject the flux consistent with the cosmologically observed dark-matter concentration for all coupling constants α_{D}<0.64, assuming a scalar dark-matter particle. We also calculate the sensitivity of future COHERENT detectors to dark-matter signals which will ambitiously test multiple dark-matter spin scenarios.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".