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Record W2775559881 · doi:10.1063/1.5006700

Low magnetic field cooling of lepton plasmas via cyclotron-cavity resonance

2017· article· en· W2775559881 on OpenAlexafffund
E. D. Hunter, N. Evetts, J. Fajans, W. N. Hardy, H. Landsberger, R. Mcpeters, J. S. Wurtele

Bibliographic record

VenuePhysics of Plasmas · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsPlasmaCyclotronAtomic physicsLeptonPenning trapElectronMagnetic fieldCyclotron resonanceElectron cyclotron resonanceLaser coolingElectron coolingNuclear physicsOptics

Abstract

fetched live from OpenAlex

Pure electron or pure positron plasmas held in magnetic fields B radiate energy because of the cyclotron motion of the plasma particles; nominally, the plasmas should cool to the often cryogenic temperatures of the trap in which they are confined. However, the cyclotron cooling rate for leptons is (1/4 s)(B/1 T)2, and significant cooling is not normally observed unless B≳1 T. Cooling to the trap temperatures of ∼10 K is particularly difficult to attain. Here, we show that dramatically higher cooling rates (×100) and lower temperatures (÷1000) can be obtained if the plasmas are held in electromagnetic cavities rather than in effectively free space conditions. We find that plasmas with up to 107 particles can be cooled in fields close to 0.15 T, much lower than 1 T commonly thought to be necessary to obtain plasma cooling. Appropriate cavities can be constructed with only minor modifications to the standard Penning-Malmberg trap structures.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.241
Teacher spread0.233 · 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 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

Citations7
Published2017
Admission routes2
Has abstractyes

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