First ultracold neutrons produced at TRIUMF
Bibliographic record
Abstract
We installed a source for ultracold neutrons at a new, dedicated spallation target at TRIUMF. The source was originally developed in Japan and uses a superfluid-helium converter cooled to $0.9\phantom{\rule{0.16em}{0ex}}\mathrm{K}$. During an extensive test campaign in November 2017, we extracted up to $325\phantom{\rule{0.16em}{0ex}}000$ ultracold neutrons after a one-minute irradiation of the target, over three times more than previously achieved with this source. The corresponding ultracold-neutron density in the whole production and guide volume is $5.3\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}3}$. The storage lifetime of ultracold neutrons in the source was initially 37 s and dropped to 24 s during the 18 days of operation. During continuous irradiation of the spallation target, we were able to detect a sustained ultracold-neutron rate of up to $1500\phantom{\rule{0.16em}{0ex}}{\mathrm{s}}^{\ensuremath{-}1}$. Simulations of UCN production, UCN transport, temperature-dependent UCN yield, and temperature-dependent storage lifetime show excellent agreement with the experimental data and confirm that the ultracold-neutron-upscattering rate in superfluid helium is proportional to ${T}^{7}$.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".