Calculation of the transfer efficiency between dual magneto-optical traps and simulation of a Ioffe trap for BoseEinstein condensation
Bibliographic record
Abstract
We consider the problem of transferring a cold atomic cloud from a low-vacuum chamber to an ultra-high-vacuum (UHV) chamber, where it can be recaptured and cooled to the transition temperature for BoseEinstein condensation (BEC). Our calculation assumes an initial MaxwellBoltzmann velocity distribution for the thermal cloud and a Gaussian spatial density distribution that is characteristic of magneto-optical traps (MOTs). Using a coordinate transformation we find the density of the recaptured atomic cloud as a function of time. This allows us to investigate the effect of experimental parameters on the transfer efficiency. These parameters include the distance of separation between the two chambers, the duration of the initial on-resonant laser used to push the thermal cloud, and the initial cloud temperature. We also present numerical simulations of the magnetic field due to a simplified IoffePritchard (IP) trap that has recently been used to obtain BEC using laser-cooling techniques. This trap converts a quadrupole magnetic field into an IP configuration using the magnetic field of a conical solenoid placed orthogonally to the axis of symmetry of a pair of quadrupole coils. Our results are suitable for small experimental groups interested in achieving BEC. PACS No.: 03.75
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".