Spin-group reclassification of neutron resonances

The evolution of the many elements formed in the Universe, from hydrogen and helium, to the ones heavier than iron, and their relative abundance, continues to be one of the main sources of scientific interest today. The different processes known in astrophysics to govern these successions of nuclear formation and decay, r-process and s-process, depend strongly on nuclear properties such as the intrinsic density of levels of a given nucleus and how they behave when emitting or absorbing particles such as neutrons, protons and/or photons. There are few experimental constraints that we can use to narrow down such properties and most of the information we know comes from experimental measurements of resonance states seen in compound nuclei formed by neutron-induced reactions. Therefore, a proper and reliable account of all resonances is crucial for the description of nuclear reactions. We have developed a Machine-Learning method, the Bayesian Resonance Reclassifier, to make use of the statistical properties of resonances to train an algorithm capable to identify missing and misassigned resonances. We are able to train the model on synthetic data and use transfer learning to assess resonance sequences from the Atlas of Neutron Resonances, evaluated files or experimental data.