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Record W2036159418 · doi:10.1103/physreva.68.033801

Superfluorescence polarization: Signature of collisional redistribution

2003· article· en· W2036159418 on OpenAlexafffund
A. Kumarakrishnan, S. Chudasama, Xianming L. Han

Bibliographic record

VenuePhysical Review A · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAtomic physicsPolarization (electrochemistry)Redistribution (election)Optical pumpingMagnetic fieldLinear polarizationLaserOpticsQuantum mechanicsChemistry

Abstract

fetched live from OpenAlex

We have studied effects of magnetic sublevel degeneracy on the polarization of superfluorescent pulses generated on the Ca $4s4p{}^{1}{P}_{1}--3d4s{}^{1}{D}_{2}$ transition at $5.5\ensuremath{\mu}\mathrm{m}.$ These pulses were generated from a cell of length 50 cm by optically pumping calcium vapor on the ${4s}^{2}{}^{1}{S}_{0}--4s4p{}^{1}{P}_{1}$ transition in the presence of Ar gas. The axis of ellipticity of superfluorescence (SF) polarization is oriented parallel to the axis of the pump-laser polarization at large detunings, and undergoes an abrupt rotation through $90\ifmmode^\circ\else\textdegree\fi{}$ for detunings close to resonance. The distribution of populations in the magnetic sublevels of the ${}^{1}{P}_{1}$ state can be estimated using a simple model based on previously calculated cross sections for collisionally aided absorption in the presence of an intense (pump) field. For large detunings, these estimates are consistent with the polarized SF intensity measured in the experiment. A direct measurement of the populations of the ${}^{1}{P}_{1}$ magnetic sublevels also supports the collisional redistribution predicted by the calculated cross sections. We therefore suggest that SF polarization can be a useful signature of collisional redistribution. However, the change in ellipticity is unexpected, and probable causes for this effect are discussed.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.367

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 science0.0000.000
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 head0.008
GPT teacher head0.290
Teacher spread0.282 · 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 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

Citations3
Published2003
Admission routes2
Has abstractyes

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