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High-volume tunable resonator for axion searches above 7 GHz

2024· article· en· W4391555738 on OpenAlexfundno aff
T. A. Dyson, Chelsea L. Bartram, Ashley Davidson, Jonah B. Ezekiel, Laura M. Futamura, Tongtian Liu, Chao-Lin Kuo

Bibliographic record

VenuePhysical Review Applied · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersSLAC National Accelerator LaboratoryNatural Sciences and Engineering Research Council of CanadaU.S. Department of EnergyNational Science Foundation
KeywordsAxionResonatorSensitivity (control systems)PhysicsResonance (particle physics)Shell (structure)Volume (thermodynamics)LambdaQ factorField (mathematics)Quality (philosophy)OptoelectronicsOpticsParticle physicsMaterials scienceElectronic engineeringEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

While haloscope experiments searching for axion dark matter with cylindrical microwave cavity resonators are the most sensitive to date, that sensitivity is degraded at high frequencies, due to geometric scaling. The authors demonstrate a prototype thin-shell cavity resonator that decouples volume from resonant frequency, and thus avoids such degradation. As the resonator comprises two mechanically isolated pieces, a protocol is developed for robust, automated precision alignment, which enables a wide tuning range for the resonator's axion-sensitive TM${}_{010}$ mode. A discussion of the instrument's feasibility for high-frequency probes of the post-inflationary scenario is also offered.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.015
GPT teacher head0.289
Teacher spread0.274 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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