Neutron Interferometry at the National Institute of Standards and Technology
Bibliographic record
Abstract
Neutron interferometry has proved to be a very precise technique for measuring the quantum mechanical phase of a neutron caused by a potential energy difference between two spatially separated neutron paths inside interferometer. The path length inside the interferometer can be many centimeters (and many centimeters apart) making it very practical to study a variety of samples, fields, potentials, and other macroscopic medium and quantum effects. The precision of neutron interferometry comes at a cost; neutron interferometers are very susceptible to environmental noise that is typically mitigated with large, active isolated enclosures. With recent advances in quantum information processing especially quantum error correction (QEC) codes we were able to demonstrate a neutron interferometer that is insensitive to vibrational noise. A facility at NIST’s Center for Neutron Research (NCNR) has just been commissioned with higher neutron flux than the NCNR’s older interferometer setup. This new facility is based on QEC neutron interferometer, thus improving the accessibility of neutron interferometry to the greater scientific community and expanding its applications to quantum computing, gravity, and material research.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".