activity
20202025
most citedNuclear masses learned from a probabilistic neural network

65 citations · 196 across the 14 of their papers we have counts for

collaborators

14 papers

nucl-ex2025

Exploration for Astromers near Sn with the Canadian Penning Trap

A. A. Valverde, S. Cupp, A. Gross +26

Nuclear isomers can have significant impacts on astrophysical nucleosynthesis processes, with recent efforts demonstrating that the population of isomeric states with different hal…

physics.app-ph2025★ 14 cited

ENDF/B-VIII.1: Updated Nuclear Reaction Data Library for Science and Applications

G. P. A. Nobre, R. Capote, M. T. Pigni +106

The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology appl…

nucl-th2025

Reducing parametric uncertainties through information geometry methods

M. Imbrišak, A. E. Lovell, M. R. Mumpower

Information geometry is a study of applying differential geometry methods to challenging statistical problems, such as uncertainty quantification. In this work, we use information…

nucl-th2025

An optical-lensing inspired data thinning method for nuclear cross section data

M. Imbrišak, A. E. Lovell, M. R. Mumpower

In the study of nuclear cross sections, the computational demands of data assimilation methods can become prohibitive when dealing with large data sets. We have developed a novel v…

nucl-th2025

Weighted Levenberg-Marquardt methods for fitting multichannel nuclear cross section data

M. Imbrišak, A. E. Lovell, M. R. Mumpower

We present an extension of the Levenberg-Marquardt algorithm for fitting multichannel nuclear cross section data. Our approach offers a practical and robust alternative to conventi…

nucl-ex2025

Astrophysical significance of the isomer Ag demonstrated through direct mass measurement

F. Rivero, M. Brodeur, J. A. Clark +18

The abundance of elements heavier than iron produced via the astrophysical rapid-neutron capture process depends sensitively on the atomic mass of the involved nuclei as well as th…

nucl-ex2024

Beta delayed neutron emission of Cd

M. Madurga, Z. Y. Xu, 1 R. Grzywacz +54

Using the time-of-flight technique, we measured the beta-delayed neutron emission of Cd. From our large-scale shell model (LSSM) calculation using the NLO interaction […

physics.ins-det2024

Novel Application of Neutrinos to Evaluate U.S. Nuclear Weapons Performance

J. R. Distel, E. C. Dunton, J. M. Durham +10

There is a growing realization that neutrinos can be used as a diagnostic tool to better understand the inner workings of a nuclear weapon. Robust estimates demonstrate that an Inv…

nucl-th2023★ 1 cited

The Los Alamos evaluation of Pu neutron-induced reactions in the fast energy range

M. R. Mumpower, D. Neudecker, T. Kawano +5

A major revision of the evaluation of Pu neutron-induced reaction cross sections is reported in the fast energy range. The evaluation starts at 2.5 keV incident neutron ene…

nucl-th2022★ 1 cited

Nuclear data activities for medium mass and heavy nuclei at Los Alamos

M. R. Mumpower, T. M Sprouse, T. Kawano +7

Nuclear data is critical for many modern applications from stockpile stewardship to cutting edge scientific research. Central to these pursuits is a robust pipeline for nuclear mod…

nucl-th2022★ 62 cited

Physically Interpretable Machine Learning for nuclear masses

M. R. Mumpower, T. M. Sprouse, A. E. Lovell +1

We present a novel approach to modeling the ground state mass of atomic nuclei based directly on a probabilistic neural network constrained by relevant physics. Our Physically Inte…

nucl-th2022★ 9 cited

Beta-delayed fission in the coupled Quasi-particle Random Phase Approximation plus Hauser-Feshbach approach

M. R. Mumpower, T. Kawano, T. M. Sprouse

Beta-delayed neutron emission and -delayed fission (df) probabilities were calculated for heavy, neutron-rich nuclei using the Los Alamos coupled Quasi-Particle Random Phase…

nucl-th2022★ 65 cited

Nuclear masses learned from a probabilistic neural network

A. E. Lovell, A. T. Mohan, T. M. Sprouse +1

Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processe…

nucl-th2020★ 44 cited

Extension of the Hauser-Feshbach Fission Fragment Decay Model to Multi-Chance Fission

A. E. Lovell, T. Kawano, S. Okumura +3

The Hauser-Feshbach fission fragment decay model, , which calculates the statistical decay of fission fragments, has been expanded to include multi-chance fission,…