| 2025 |
G. P. A. Nobre, R. Capote, M. T. Pigni, A. Trkov, et al. |
ENDF/B-VIII.1: updated nuclear reaction data library for science and applications |
accepted |
|
| 2025 |
M. Imbrišak, A. Lovell, M. Mumpower |
Reducing parametric uncertainties through information geometry methods |
submitted |
|
| 2025 |
M. Imbrišak, A. Lovell, M. Mumpower |
An optical-lensing inspired data thinning method for nuclear cross section data |
submitted |
|
| 2025 |
M. Imbrišak, A. Lovell, M. Mumpower |
Weighted Levenberg-Marquardt methods for fitting multichannel nuclear cross section data |
submitted |
|
| 2024 |
W. S. Porter, B. Liu, D. Ray, A. A. Valverde, et al. |
Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling |
PRC 110 034321 |
|
| 2023 |
M. Mumpower, M. Li, T. M. Sprouse, B. S. Meyer, et al. |
Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach |
Fr. in Phys. |
|
| 2023 |
M. Mumpower, D. Neudecker, H. Sasaki, T. Kawano, et al. |
Collective enhancement in the exciton model |
PRC 107 034606 |
|
| 2022 |
H. Schatz, A. D. Becerril Reyes, A. Best, E. Brown, et al. |
Horizons: nuclear astrophysics in the 2020s and beyond |
J. Phys. G 49 110502 |
|
| 2022 |
M. Mumpower, T. M. Sprouse, A. Lovell, A. T. Mohan |
Physically Interpretable Machine Learning for nuclear masses |
PRCL 106 021301 |
|
| 2022 |
A. Lovell, A. T. Mohan, T. M. Sprouse, M. Mumpower |
Nuclear masses learned from a probabilistic neural network |
PRC 106 014305 |
|
| 2021 |
A. Lovell, T. Kawano, S. Okumura, I. Stetcu, et al. |
Extension of the Hauser-Feshbach fission fragment decay model to multi-chance fission |
PRC 103 014615 |
|
| No papers in this section match the filter. |
