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Record W3095007953 · doi:10.1088/1402-4896/abc581

Engineering of the Cesium Zeeman sublevel populations using sequences of laser pulses and RF excitation

2020· article· en· W3095007953 on OpenAlexaff
Farrokh Sarreshtedari, Alireza Rashedi, Fahime Ghashghaei, Mahmood Sabooni

Bibliographic record

VenuePhysica Scripta · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of Waterloo
FundersIran National Science Foundation
KeywordsZeeman effectPopulationCaesiumAtomic physicsLaserPhysicsExcitationPulse (music)Materials scienceOpticsMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Incorporating the rate equations of the laser pumped atomic system, the population evolution of the Cesium Zeeman sublevels is calculated and discussed when a specific sequence of laser pulses irradiate the Cesium vapor. In this scheme, after the equalization of the sublevel populations by a magnetically resonant RF pulse, using a left (right) circularly polarized laser pulse the whole atomic population transfer to high (low) projection numbers. Then by irradiating a right (left) polarized laser pulse with appropriate duration and intensity, the desired population distribution could be obtained. The effects of different sequence parameters on the population distribution of Zeeman sublevels are investigated and it is shown that by proper process adjustment it is possible to arbitrary engineer the population distribution. Preparing the desired distribution of the population in the atomic sublevels is substantially important in different light–matter interaction experiments.

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 score0.004

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 head0.088
GPT teacher head0.296
Teacher spread0.208 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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