High-precision two-dimensional atom localization from four-wave mixing in a double-Λ four-level atomic system
Bibliographic record
Abstract
Abstract We propose a scheme for high-precision two-dimensional (2D) atom localization via the four-wave mixing (FWM) in a four-level double- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mstyle displaystyle="false"> <mml:mrow> <mml:mi mathvariant="normal">Λ</mml:mi> </mml:mrow> </mml:mstyle> </mml:math> atomic system. Due to the position-dependent atom-field interaction, the 2D position information of the atoms can be directly determined by the measurement of the normalized light intensity of output FWM-generated field. We further show that, when the position-dependent generated FWM field has become sufficiently intense, efficient back-coupling to the FWM generating state becomes important. This back-coupling pathway leads to competitive multiphoton destructive interference of the FWM generating state by three supplied and one internally generated fields. We find that the precision of 2D atom localization can be improved significantly by the multiphoton destructive interference and depends sensitively on the frequency detunings and the pump field intensity. Interestingly enough, we show that adjusting the frequency detunings and the pump field intensity can modify significantly the FWM efficiency, and consequently lead to a redistribution of the atoms. As a result, the atom can be localized in one of four quadrants with holding the precision of atom localization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".