Astrophysical Constraints on the Symmetry Energy and the Neutron Skin of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mmultiscripts><mml:mrow><mml:mi>Pb</mml:mi></mml:mrow><mml:mprescripts/><mml:none/><mml:mrow><mml:mn>208</mml:mn></mml:mrow></mml:mmultiscripts></mml:mrow></mml:math> with Minimal Modeling Assumptions
Bibliographic record
Abstract
The symmetry energy and its density dependence are crucial inputs for many nuclear physics and astrophysics applications, as they determine properties ranging from the neutron-skin thickness of nuclei to the crust thickness and the radius of neutron stars. Recently, PREX-II reported a value of $0.283\ifmmode\pm\else\textpm\fi{}0.071\text{ }\text{ }\mathrm{fm}$ for the neutron-skin thickness of $^{208}\mathrm{Pb}$, implying a slope parameter $L=106\ifmmode\pm\else\textpm\fi{}37\text{ }\text{ }\mathrm{MeV}$, larger than most ranges obtained from microscopic calculations and other nuclear experiments. We use a nonparametric equation of state representation based on Gaussian processes to constrain the symmetry energy ${S}_{0}$, $L$, and ${R}_{\mathrm{skin}}^{^{208}\mathrm{Pb}}$ directly from observations of neutron stars with minimal modeling assumptions. The resulting astrophysical constraints from heavy pulsar masses, LIGO/Virgo, and NICER clearly favor smaller values of the neutron skin and $L$, as well as negative symmetry incompressibilities. Combining astrophysical data with PREX-II and chiral effective field theory constraints yields ${S}_{0}=33.{0}_{\ensuremath{-}1.8}^{+2.0}\text{ }\text{ }\mathrm{MeV}$, $L=5{3}_{\ensuremath{-}15}^{+14}\text{ }\text{ }\mathrm{MeV}$, and ${R}_{\mathrm{skin}}^{^{208}\mathrm{Pb}}=0.1{7}_{\ensuremath{-}0.04}^{+0.04}\text{ }\text{ }\mathrm{fm}$.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".