Simulation of stimulated Raman adiabatic passage from the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mi>S</mml:mi> </mml:mrow> </mml:math> to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>S</mml:mi> </mml:mrow> </mml:math> states of the hydrogen and antihydrogen atoms
Bibliographic record
Abstract
Achieving a high population of antihydrogen and hydrogen atoms in the $2S$ level is essential for spectroscopy measurements testing similarities between matter and antimatter. We propose and examine the efficiency of applying the STIRAP (stimulated Raman adiabatic passage) process in achieving high population transfer from the 1$S$ to the $2S$ levels. We utilized a circularly polarized Lyman alpha $(\mathrm{Ly}\text{\ensuremath{-}}\ensuremath{\alpha})$ pulse to couple the $1S$ state to the $2P$ state and a microwave pulse to couple the $2P$ state and the $2S$ state. We calculate the efficiency of the STIRAP process for transferring the population between the stretched states $(1{S}_{d},2{S}_{d})$ as a function of experimental parameters such as Rabi frequencies and pulse durations. We find that a $\mathrm{Ly}\text{\ensuremath{-}}\ensuremath{\alpha}$ pulse with an energy of a few nanojoules could produce nearly perfect transfer at zero detunings for atoms on the laser beam axis. We extended the analysis to a thermal ensemble of atoms, where Doppler detuning affects the velocity distribution of the hydrogen atoms produced in the $2S$ level. We found that the width of such velocity distribution is controlled by the Rabi frequency. We show that the peak velocity of the hydrogen atoms in the $2S$ level after STIRAP can be controlled by the $\mathrm{Ly}\text{\ensuremath{-}}\ensuremath{\alpha}$ pulse detuning. The efficiency of STIRAP in transferring population increases at low temperature $(T\ensuremath{\sim}1\phantom{\rule{0.16em}{0ex}}\mathrm{mK})$. Finally, we show that a background magnetic field improves the transfer rates between the other trappable states $(1{S}_{c},2{S}_{c})$.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".