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Record W3053640896 · doi:10.1088/1361-6455/abb030

Intensity–intensity correlations and low-frequency quantum beats in three-level systems

2020· article· en· W3053640896 on OpenAlexafffund
Christopher DiLoreto, Chitra Rangan

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsIntensity (physics)Quantum beatsQuantumAtomic physicsComputational physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract We theoretically investigate two-time intensity–intensity correlation spectroscopy in three-level atoms, a spectroscopic method that allows for direct detection of energy level structures and resonances in complex molecules. We show that in three-level Λ or V -systems, a richer spectroscopic signature is obtained when the applied fields are detuned from the transitions to values other than the typical zero-detuning or two-photon detuning settings. This allows us to identify, in the limit in which the overall decoherence rate from an excited state is much smaller than the Rabi frequency of the driving field, areas of parameter space for a three-level V -system in which low-frequency quantum beats can be generated while still maintaining significant signal amplitude. This may prove advantageous in improving the quality and ease of measurements in quantum beat spectroscopy as this type of experimental technique requires a high degree of time resolution.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations0
Published2020
Admission routes2
Has abstractyes

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