Jordan McDonnell

This technical memorandum compiles the contributions presented at the 2026 R-Matrix Workshop on Methods and Applications, held July 13–17, 2026, at Oak Ridge National Laboratory. The workshop brought together researchers working in phenomenological R-matrix theory, experimental nuclear physics, nuclear-data evaluation, uncertainty quantification, and computational methods, with particular emphasis on neutron-induced reactions. The collected contributions cover foundational R-matrix theory and experimental techniques; resolved- and unresolved-resonance-region analyses; automated resonance fitting and uncertainty quantification; statistical and parametric approaches to nuclear-data evaluation; and relativistic, multistep, and distant-pole formulations. Applications to specific compound-nuclear systems, charged-particle reactions, thermal scattering, neutron transmission, and nuclear-data validation are also presented. The memorandum documents recent developments in widely used analysis and processing tools, including EDA, AZURE2, SAMMY, AMPX, and SCALE, together with emerging Python-based implementations and new optimization methods. Additional contributions describe experimental capabilities and measurements at facilities including the Los Alamos Neutron Science Center and the Spallation Neutron Source, including the VENUS neutron time-of-flight instrument. Collectively, these contributions provide a representative account of current R-matrix methods, practical evaluation challenges, and emerging research directions. This memorandum is intended to preserve the technical content of the workshop and to serve as a reference for experimentalists, theorists, nuclear-data evaluators, software developers, students, and users of evaluated nuclear data in applications ranging from nuclear astrophysics to criticality safety.

The U.S. Department of Energy's Office of Scientific and Technical Information

The U.S. Department of Energy's Office of Scientific and Technical Information

The U.S. Department of Energy's Office of Scientific and Technical Information

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.