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Achieving a near-ideal silicon crystal neutron interferometer using submicrometer fabrication techniques

2024· article· en· W4403887014 on OpenAlexafffund
M. G. Huber, B. Heacock, Ivar Taminiau, D. G. Cory, Dusan Sarenac, R. Valdillez, D. A. Pushin

Bibliographic record

VenuePhysical Review Research · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of Waterloo
FundersNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaNational Institute of Standards and TechnologyCanada First Research Excellence FundCanada Excellence Research Chairs, Government of CanadaU.S. Department of Energy
KeywordsInterferometryOpticsAstronomical interferometerInterferometric visibilityAtom interferometerMach–Zehnder interferometerFabricationMaterials scienceNeutronPerfect crystalIntensity interferometerPhysicsCrystal (programming language)Computer scienceBent molecular geometry

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.435
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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