Scintillating bolometers for fast neutron spectroscopy in rare events searches
Bibliographic record
Abstract
Neutrons are a relevant background in rare events physics. Detectors based on fast neutron-induced nuclear reactions are commonly used for fast neutron spectroscopy. In this subject, scintillating bolometers provide an excellent energy resolution and particle discrimination by the simultaneous measurement of the heat and emitted light. Our group has constructed several 6 Li and 10 B based massive scintillating bolometers (LiF, Li 6 Eu(BO 3 ) 3 , Li 6 Gd(BO 3 ) 3 ), with energy resolutions ranging from 16 to 200 keV. First results of a 32 gr 6 LiF scintillating bolometer enriched at 95% in 6 Li operated at 20 mK are presented. The use of this material in a multi-target cryogenic dark matter experiment, like EURECA, would allow monitoring the incident neutron flux in the detector during the data-taking.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".