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Record W3104442944 · doi:10.1088/1361-6455/abcb51

A superconducting Fabry–Perot cavity for trapping cold molecules

2020· article· en· W3104442944 on OpenAlexafffund
Fatemeh S. Tahsildaran F, Manish Vashishta, Amir Hossein Farahbod, Rasoul Malekfar, Pavle Djuricanin, Katsunari Enomoto, Takamasa Momose

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMicrowaveFabry–Pérot interferometerTrappingSuperconductivityIonizationResonance (particle physics)Stark effectMaterials scienceMicrowave cavityAtomic physicsChemical polarityTrap (plumbing)MoleculeIonOptoelectronicsChemistryElectric fieldPhysicsCondensed matter physics

Abstract

fetched live from OpenAlex

Abstract A superconducting Fabry–Perot microwave cavity for a molecular trap was designed, constructed and characterized. The cavity was designed to create intense microwave fields that are sufficient to trap cold polar molecules by the AC Stark force. By coating the mirror surfaces with a superconducting material, an unloaded quality factor of up to 1.1 × 106 at 24.087 GHz was achieved at a temperature of 2.24 K. The field strength of 1.06 MV m−1 obtained for a TEM02 transverse mode with an input power of 10 W is sufficiently intense to trap ammonia molecules at a temperature of 85 mK, which is achievable by a conventional Stark molecular decelerator. We have also implemented an ion optics assembly for sensitive detection of molecules inside the microwave cavity by resonance enhanced multi-photon ionization.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.250
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2020
Admission routes2
Has abstractyes

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