Diffuse scattering model of ultracold neutrons on wavy surfaces
Bibliographic record
Abstract
Metal tubes plated with nickel-phosphorus are used in many fundamental physics experiments that use ultracold neutrons (UCN) because of their ease of fabrication. These tubes are usually polished to an average roughness of $25\text{--}150\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$. However, there is no scattering model that accurately describes UCN scattering on such a rough guide surface with a mean-square roughness greater than $5\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$. We, therefore, developed a scattering model for UCN in which scattering from random surface waviness with a size larger than the UCN wavelength is described by a microfacet Bidirectional Reflectance Distribution Function model (mf-BRDF model), and scattering from smaller structures by the Lambert's cosine law (Lambert model). For the surface waviness, we used the statistical distribution of surface slope measured by an atomic force microscope on a sample piece of guide tube as an input of the model. This model was used to describe UCN transmission experiments conducted at the pulsed UCN source in J-PARC. In these experiments, a UCN beam collimated to a divergence angle smaller than $\ifmmode\pm\else\textpm\fi{}{6}^{\ensuremath{\circ}}$ was directed into a guide tube with a mean-square roughness of 6.4 to $17\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$ at an oblique angle, and the UCN transport performance and its time-of-flight distribution were measured while changing the angle of incidence. The mf-BRDF model combined with the Lambert model with scattering probability ${p}_{L}=0.039\ifmmode\pm\else\textpm\fi{}0.003$ reproduced the experimental results well. We have thus established a procedure to evaluate the characteristics of UCN guide tubes with a surface roughness of approximately $10\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".