<i>Ab Initio</i>Description of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi></mml:math>-Shell Hypernuclei
Bibliographic record
Abstract
We present the first ab initio calculations for $p$-shell single-$\mathrm{\ensuremath{\Lambda}}$ hypernuclei. For the solution of the many-baryon problem, we develop two variants of the no-core shell model with explicit $\mathrm{\ensuremath{\Lambda}}$ and ${\mathrm{\ensuremath{\Sigma}}}^{+},{\mathrm{\ensuremath{\Sigma}}}^{0},{\mathrm{\ensuremath{\Sigma}}}^{\ensuremath{-}}$ hyperons including $\mathrm{\ensuremath{\Lambda}}\text{\ensuremath{-}}\mathrm{\ensuremath{\Sigma}}$ conversion, optionally supplemented by a similarity renormalization group transformation to accelerate model-space convergence. In addition to state-of-the-art chiral two- and three-nucleon interactions, we use leading-order chiral hyperon-nucleon interactions and a recent meson-exchange hyperon-nucleon interaction. We validate the approach for $s$-shell hypernuclei and apply it to $p$-shell hypernuclei, in particular to $_{\mathrm{\ensuremath{\Lambda}}}^{7}\mathrm{Li}$, $_{\mathrm{\ensuremath{\Lambda}}}^{9}\mathrm{Be}$, and $_{\mathrm{\ensuremath{\Lambda}}}^{13}\mathrm{C}$. We show that the chiral hyperon-nucleon interactions provide ground-state and excitation energies that generally agree with experiment within the cutoff dependence. At the same time we demonstrate that hypernuclear spectroscopy provides tight constraints on the hyperon-nucleon interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".