A Map for Atomic Nuclei express

Original: "The East Lansing Model: a Bayesian uncertainty quantified optical potential for rare isotopes"
· K. Beyer, F. M. Nunes
arXiv:2603.28599 · 2026-03-30 · CC BY 4.0 · 1 min
Scientists have built a reliable model describing how neutrons and protons scatter off nuclei.

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When a neutron or proton enters an atomic nucleus, it behaves like a ball hitting a rough wall: it can bounce off in different ways, depending on where it hits. Physicists describe such collisions using the “optical model”—it replaces the complex nucleus with an effective potential, as if we described the wall by a single average height rather than every indentation.

The new model, called the East Lansing Model (ELM), refines this potential for neutrons and protons. Its distinctive feature is a new way of accounting for the difference between neutrons and protons in the nucleus. The authors used data on neutron and proton scattering on spherical nuclei with mass number A ≥ 40 and beam energies of 10–100 MeV. It turned out that adding data on (p,n) reactions (where a proton turns into a neutron) by itself does not change the parameters much, but introducing a new parameter related to the “neutron skin” (the excess of neutrons on the nucleus surface) makes the model more accurate.

The neutron skin is like a thin layer of foam on the surface of coffee: in heavy nuclei with an excess of neutrons, the outer layer is enriched with neutrons, and this affects scattering.

The model is especially useful for nuclei far from stability, up to the boundaries of the proton and neutron drip lines (where nuclei can no longer hold extra nucleons). ELM extrapolations give smaller uncertainties than other global optical potentials, which is important for astrophysics and the physics of rare isotopes.

Original abstract

as written by the authors arXiv:2603.28599

The East Lansing Model is a global, uncertainty-quantified optical potential for neutron and proton projectiles, with a novel form for the neutron-proton asymmetry component, with the goal to improve extrapolations away from stability. Our Bayesian calibration relies on (n,n), (p,p) and (p,n) experimental data for angular distributions on spherical targets with mass $$A\geq 40$$, and beam energies in the range $$E = 10-100$$ MeV. When considering the stable nuclei for which data is available, our results demonstrate that the inclusion of the $$(p,n)$$ data alone does not significantly change the parameterization. The additional information contained in (p,n) only becomes evident by introducing a new parameterization, one that gives the flexibility to encode neutron skins in the optical potential through an asymmetry dependent term. Finally, extrapolations of ELM toward the limits of stability (namely toward the proton and neutron driplines) leads to reduced uncertainties when compared to other global optical potentials in use.

The optical model of the nucleus got its name from an analogy with the scattering of light on a translucent sphere: the nucleus partially absorbs and partially scatters particles, like frosted glass does with light.

Scientists
Emmy NoetherWolfgang PauliEnrico FermiPaul DiracChien-Shiung WuEugene Wigner
Concepts
evidential neural networks probabilistic model parameter estimation Standard Model uncertainty estimation probability theory generalization neural network polarimetry bayesian evidence machine learned interatomic potential maximum likelihood probabilistic forecast Bayesian inference neutron skin nuclear stability posterior distribution nested sampling Large Language Models Poisson distribution molecular property prediction