| 2023 |
M. Li, T. M. Sprouse, B. S. Meyer, M. Mumpower |
Atomic masses with Machine Learning for the astrophysical r process |
PLB 138385 |
|
| 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. |
|
| 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 |
|
| 2022 |
N. Vassh, G. C. McLaughlin, M. Mumpower, R. Surman |
The need for a local nuclear physics feature in the neutron-rich rare-earths to explain solar $r$-process abundances |
submitted |
|
| 2020 |
M. Vilen, J. M. Kelly, A. Kankainen, M. Brodeur, et al. |
Exploring the mass surface near the rare-earth abundance peak via precision mass measurements at JYFLTRAP |
PRC 101, 034312 |
|
| 2018 |
M. Vilen, J. M. Kelly, A. Kankainen, M. Brodeur, et al. |
Improving $r$-process calculations for the rare-earth abundance peak via mass measurements at JYFLTRAP |
PRL 120, 262701 |
|
| 2018 |
R. Orford, N. Vassh, J. Clark, G. C. McLaughlin, et al. |
Precision mass measurements of neutron-rich neodymium and samarium isotopes and their role in understanding rare-earth peak formation |
PRL 120, 262702 |
|
| 2015 |
M. Mumpower, R. Surman, D. L. Fang, M. Beard, et al. |
The impact of individual nuclear masses on $r$-process abundances |
Phys. Rev. C 92 035807 |
|
| 2015 |
M. Mumpower, R. Surman, D. L. Fang, M. Beard, et al. |
The impact of uncertain nuclear masses near closed shells on the $r$-process abundance pattern |
J. Phys. G 42 034027 |
|
| 2014 |
A. Aprahamian, I. Bentley, M. Mumpower, R. Surman |
Sensitivity studies for a main $r$ process: nuclear masses |
AIP Advances 4, 041101 |
|