Bibliographic record
Abstract
We examine single-frequency optical schemes for species-selective trapping of ultracold alkali-metal atoms. Independently addressing the elements of a binary mixture enables the creation of an optical lattice for one atomic species with little or no effect on the other. We analyze a ``tune-in'' scheme, using near-resonant detuning to create a strong potential for one specific element. A ``tune-out'' scheme is also developed, in which the trapping wavelength is chosen to lie between two strong transitions of an alkali-metal atom such that the induced dipole moment is zero for that species but is nonzero for any other. We compare these schemes by examining the trap depths and heating rates associated with both. We find that the tune-in scheme is preferable for $\mathrm{Li}\text{\ensuremath{-}}\mathrm{Na}$, $\mathrm{Li}\text{\ensuremath{-}}\mathrm{K}$, and $\mathrm{K}\text{\ensuremath{-}}\mathrm{Na}$ mixtures, while the tune-out scheme is preferable for $\mathrm{Li}\text{\ensuremath{-}}\mathrm{Cs}$, $\mathrm{K}\text{\ensuremath{-}}\mathrm{Rb}$, $\mathrm{Rb}\text{\ensuremath{-}}\mathrm{Cs}$, $\mathrm{K}\text{\ensuremath{-}}\mathrm{Cs}$, and $^{39}\mathrm{K}\text{\ensuremath{-}}^{40}\mathrm{K}$ mixtures. Several applications of species-selective optical lattices are explored, including the creation of a lattice for a single species in the presence of a phononlike background, the tuning of relative effective mass, and the isothermal increase of phase-space density.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".