Polarized Cold-neutron Reflectometry at JRR-3/MINE2 for the Development of Ultracold-neutron Spin Analyzers for a Neutron EDM Experiment at TRIUMF
Bibliographic record
Abstract
The neutron electric dipole moment (EDM) is a sensitive probe for currently undiscovered sources of charge-parity symmetry violation. As part of the TRIUMF Ultracold Advanced Neutron (TUCAN) collaboration, we are developing spin analyzers for ultracold neutrons (UCNs) to be used for a next-generation experiment to measure the neutron EDM with unprecedented precision. Spin-state analysis of UCNs constitutes an essential part of the neutron EDM measurement sequence. Magnetized iron films used as spin filters of UCNs are crucial experimental components, whose performance directly influences the statistical sensitivity of the measurement. To test such iron film spin filters, we propose the use of polarized cold-neutron reflectometry, in addition to conventional UCN transmission experiments. The new method provides information on iron film samples complementary to the UCN tests and accelerates the development cycles. We developed a collaborative effort to produce iron film spin filters and test them with cold and ultracold neutrons available at JRR-3/MINE2 and J-PARC/MLF BL05. In this article, we review the methods of neutron EDM measurements, discuss the complementarity of this new approach to test UCN spin filters, provide an overview of our related activities, and present the first results of polarized cold-neutron reflectometry recently conducted at the MINE2 beamline.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".