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Record W2767351954 · doi:10.1103/physreva.96.051401

Optical gain in rotationally excited nitrogen molecular ions

2017· article· en· W2767351954 on OpenAlexaff
A. Azarm, P. B. Corkum, Pavel Polynkin

Bibliographic record

VenuePhysical review. A/Physical review, A · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsJoint Attosecond Science Laboratory
FundersAir Force Office of Scientific ResearchMultidisciplinary University Research InitiativeU.S. Air Force
KeywordsPopulation inversionExcited stateAtomic physicsIonPopulationFemtosecondWavelengthStimulated emissionPhysicsSpectroscopyAmplified spontaneous emissionLaserSpontaneous emissionIsotropyExcited electronic stateOptical pumpingMaterials scienceOptics

Abstract

fetched live from OpenAlex

We pump low-pressure nitrogen gas with ionizing femtosecond laser pulses at 1.5 $\ensuremath{\mu}\mathrm{m}$ wavelength. The resulting rotationally excited ${\mathrm{N}}_{2}^{+}$ molecular ions generate directional, forward-propagating stimulated and isotropic spontaneous emissions at 428 nm wavelength. Through high-resolution spectroscopy of these emissions, we quantify rotational population distributions in the upper and lower emission levels. We show that these distributions are shifted with respect to each other, which has a strong influence on the transient optical gain in this system. Although we find that electronic population inversion exists in our particular experiment, we show that sufficient dissimilarity of rotational distributions in the upper and lower emission levels could, in principle, lead to gain without net electronic population inversion.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.392
Teacher spread0.376 · 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

Citations73
Published2017
Admission routes1
Has abstractyes

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