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Record W2994919889 · doi:10.1088/1361-6455/ab604f

Dissipative dynamics of atomic and molecular Rydberg gases: Avalanche to ultracold plasma states of strong coupling

2019· article· en· W2994919889 on OpenAlexafffund
M. Aghigh, K. M. Grant, Rafael Haenel, Kevin L. Marroquin, Fernanda B. V. Martins, H. Sadegi, Markus Schulz-Weiling, John Sous, R. Wang, James S. Keller, Edward R. Grant

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of British Columbia
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsAtomic physicsRydberg formulaMetastabilityPolyatomic ionPlasmaPhysicsXenonIonizationPrincipal quantum numberChemistryIonQuantumNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Not long after metastable xenon was photoionized in a magneto-optical trap, groups in Europe and North America found that similar states of ionized gas evolved spontaneously from state-selected, high principal quantum number Rydberg gases. Studies of atomic xenon and molecular nitric oxide entrained in a supersonically cooled molecular beam subsequently showed much the same final state evolved from a sequence of prompt Penning ionization and electron-impact avalanche to plasma, well-described by coupled rate-equation simulations. But, measured over longer times, the molecular ultracold plasma was found to exhibit an anomalous combination of very long lifetime and very low apparent electron temperature. This review first summarizes early developments in the general study of ultracold plasmas formed by atomic and molecular Rydberg gases, and then turns to describe the particular properties of the nitric oxide molecular ultracold plasma that appear to call for an explanation beyond the realm of conventional plasma physics.

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 categoriesMeta-epidemiology (narrow)
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 score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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.

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

Citations15
Published2019
Admission routes2
Has abstractyes

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