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Record W2956882135 · doi:10.1103/physreva.100.013406

Short- and long-term gain dynamics in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mi mathvariant="normal">N</mml:mi><mml:mn>2</mml:mn><mml:mo>+</mml:mo></mml:msubsup></mml:math> air lasing

2019· article· lv· W2956882135 on OpenAlexafffund
Mathew Britton, Marianna Lytova, Patrick Laferrière, Peng Peng, Felipe Morales, Dong Hyuk Ko, Maria Richter, Pavel Polynkin, D. M. Villeneuve, Chunmei Zhang, Misha Ivanov, Michael Spanner, Ladan Arissian, P. B. Corkum

Bibliographic record

VenuePhysical review. A/Physical review, A · 2019
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersArmy Research OfficeAir Force Office of Scientific ResearchEngineering and Physical Sciences Research CouncilNational Research Council CanadaMultidisciplinary University Research InitiativeGovernment of Ontario
KeywordsExcited statePhysicsPopulation inversionAtomic physicsPopulationLasing thresholdWave packetElectronScatteringElectron captureNuclear physicsOpticsLaser

Abstract

fetched live from OpenAlex

Air lasing in the nitrogen molecular ion is not well understood because the complex physics responsible for gain is interwoven with pulse propagation in an extreme environment. Here we use a short gas jet to limit the interaction length, thereby removing the propagation effects. We report on several mechanisms that contribute to the decay of gain in different conditions, and experimentally isolate two decay timescales: the decay of long-term gain due to collisional state mixing, and short-term gain that cannot be explained by population inversion. To test the former, we control the inelastic electron scattering rate by varying the gas concentration while keeping the propagation length fixed, and predict the change of the decay using a model of collisional state mixing. We show that the same mechanism causes the decay of rotational wave packets in the states of the ion. Finally, we simulate the complex modulations of gain due to rotational wave packets and the propagation of the probe pulse through the evolving rotationally excited and inverted medium.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.005

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 head0.286
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations17
Published2019
Admission routes2
Has abstractyes

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