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Record W4293868640 · doi:10.1109/ims37962.2022.9865387

Rydberg Atomic Electrometry: A Near-Field Technology for Complete Far-Field Imaging in Seconds?

2022· article· en· W4293868640 on OpenAlexaff
Donald Booth, Kent Nickerson, Stephanie M. Bohaichuk, Jennifer Erskine, James P. Shaffer

Bibliographic record

Venue2022 IEEE/MTT-S International Microwave Symposium - IMS 2022 · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRydberg formulaElectric fieldPhysicsRydberg atomNear and far fieldBandwidth (computing)Terahertz radiationSensitivity (control systems)Atomic physicsOpticsOptoelectronicsTelecommunicationsComputer scienceElectronic engineeringEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

In this presentation, we describe how Rydberg states can be used for electromagnetic field sensing in the near field with minimal field distortion over a frequency range spanning almost six orders of magnitude, including from GHz to THz. We outline how Rydberg atoms packaged in electromagnetically transparent vapor cells can be used for high-sensitivity, absolute, self-calibrated sensing of electric fields. We present testing data on a Rydberg atom-based sensor prototype we have constructed and discuss applications like imaging, including the influence of vapor cells. In particular we study the effect of an interferer near the sensing frequency while demonstrating a wide carrier bandwidth. Taking limitations and current technology into consideration, the state-of-the-art Rydberg atom system presented here is promising for over-the-air test and measurement applications.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

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 studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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
Published2022
Admission routes1
Has abstractyes

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