Feshbach resonances in ultracold<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mmultiscripts><mml:mi mathvariant="normal">Rb</mml:mi><mml:mprescripts/><mml:none/><mml:mn>85</mml:mn></mml:mmultiscripts><mml:mtext>−</mml:mtext><mml:mmultiscripts><mml:mi mathvariant="normal">Rb</mml:mi><mml:mprescripts/><mml:none/><mml:mn>87</mml:mn></mml:mmultiscripts></mml:mrow></mml:math>and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mmultiscripts><mml:mi mathvariant="normal">Li</mml:mi><mml:mprescripts/><mml:none/><mml:mn>6</mml:mn></mml:mmultiscripts><mml:mtext>−</mml:mtext><mml:mmultiscripts><mml:mi mathvariant="normal">Rb</mml:mi><mml:mprescripts/><mml:none/><mml:mn>87</mml:mn></mml:mmultiscripts></mml:mrow></mml:math>mixtures
Why this work is in the frame
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Bibliographic record
Abstract
We present an analysis of experimentally accessible magnetic Feshbach resonances in ultracold heteronuclear $^{85}\mathrm{Rb}\text{\ensuremath{-}}^{87}\mathrm{Rb}$ and $^{6}\mathrm{Li}\text{\ensuremath{-}}^{87}\mathrm{Rb}$ mixtures. Using recent experimental measurements of the triplet scattering lengths for $^{6}\mathrm{Li}\text{\ensuremath{-}}^{87}\mathrm{Rb}$ and $^{7}\mathrm{Li}\text{\ensuremath{-}}^{87}\mathrm{Rb}$ mixtures and Feshbach resonances for one combination of atomic states, we create model potential curves and fine-tune them to reproduce the measured resonances and to predict the location of several experimentally relevant resonances in Li-Rb collisions. To model $^{85}\mathrm{Rb}\text{\ensuremath{-}}^{87}\mathrm{Rb}$ collisions, we use accurate ${\mathrm{Rb}}_{2}$ potentials obtained previously from the analysis of experiments on $^{87}\mathrm{Rb}\text{\ensuremath{-}}^{87}\mathrm{Rb}$ collisions. We find resonances that occur at very low magnetic fields, below $10\phantom{\rule{0.3em}{0ex}}\mathrm{G}$, which may be useful for entanglement generation in optical lattices or atom chip magnetic traps.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.007 | 0.013 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.285 | 0.010 |
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 it