Achieving a near-ideal silicon crystal neutron interferometer using submicrometer fabrication techniques
Bibliographic record
Abstract
Perfect-crystal neutron interferometry, which is analogous to Mach-Zehnder interferometry, uses Bragg diffraction to form interfering neutron paths. The measured phase shifts can be used to probe many types of interactions whether it be nuclear, electromagnetic, gravitational, or topological in nature. For a perfect-crystal interferometer to preserve coherence, the crystal must possess a high degree of dimensional tolerance as well as being relatively defect-free with minimal internal stresses. In the past, perfect-crystal neutron interferometers have been produced by a two-step process. First, a resin diamond wheel would be used to remove excess material and shape the interferometer. Afterword, the crystal would be etched to remove surface defects and elevate strains. This process has had limitations in terms of repeatability and in maximizing the final contrast, or fringe visibility, of the interferometer. We have tested various fabrication and post-fabrication techniques on a single perfect-crystal neutron interferometer and measured the interferometer's performance at each step. Here we report a robust, nonetching fabrication process with high final contrast. For the interferometer used in this work, we achieved contrasts of greater than 90% several times and ultimately finished with an interferometer that has 92% contrast and a uniform phase distribution. Published by the American Physical Society 2024
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".