Designing and Building a Low Energy Neutron Source
Bibliographic record
Abstract
The Department of Physics, together with the NEWS-G collaboration at Queen’s University are developing Spherical Proportional Counters (SPC) aimed for dark matter detection research. The response of SPCs to nuclear recoils with the interaction of the hypothetical dark matter particles can be best calibrated with a high intensity beams of medium energy neutrons (~10 keV – 100 keV). Presently, the number of facilities having such neutron sources are quite low. This project aims to design and build a medium energy neutron source at the proton accelerator facility of the Reactor Materials Testing Laboratory (RMTL). The proton accelerator at RMTL can provide protons with energies up to 8 MeV and a current of 35 µA. Our beamline will utilize protons of around 2 MeV energy and a maximum current of 20 µA. This high current will provide us more neutron intensity compared to other facilities. As the neutron intensity increases, the probability of them interacting with the gas molecules in the SPC also increases giving the physics department a better chance of calibrating.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".