Direct comparison between Bayesian and frequentist uncertainty quantification for nuclear reactions GB King, AE Lovell, L Neufcourt, FM Nunes
Physical review letters 122 (23), 232502, 2019
83 2019 Fission fragment decay simulations with the CGMF code P Talou, I Stetcu, P Jaffke, ME Rising, AE Lovell, T Kawano
Computer Physics Communications 269, 108087, 2021
56 2021 Measurement of the prompt fission neutron spectrum from 10 keV to 10 MeV induced by neutrons of energy 1–20 MeV KJ Kelly, M Devlin, JM O'Donnell, JA Gomez, D Neudecker, RC Haight, ...
Physical Review C 102 (3), 034615, 2020
44 2020 Uncertainty quantification for optical model parameters AE Lovell, FM Nunes, J Sarich, SM Wild
Physical Review C 95 (2), 024611, 2017
42 2017 Constraining transfer cross sections using Bayes' theorem AE Lovell, FM Nunes
Physical Review C 97 (6), 064612, 2018
38 2018 Toward emulating nuclear reactions using eigenvector continuation C Drischler, M Quinonez, PG Giuliani, AE Lovell, FM Nunes
Physics Letters B 823, 136777, 2021
37 2021 Systematic uncertainties in direct reaction theories AE Lovell, FM Nunes
Journal of Physics G: nuclear and particle physics 42 (3), 034014, 2015
36 2015 Preequilibrium Asymmetries in the Prompt Fission Neutron Spectrum KJ Kelly, T Kawano, JM O’Donnell, JA Gomez, M Devlin, D Neudecker, ...
Physical Review Letters 122 (7), 072503, 2019
33 2019 Extension of the Hauser-Feshbach fission fragment decay model to multichance fission AE Lovell, T Kawano, S Okumura, I Stetcu, MR Mumpower, P Talou
Physical Review C 103 (1), 014615, 2021
32 2021 Nuclear masses learned from a probabilistic neural network AE Lovell, AT Mohan, TM Sprouse, MR Mumpower
Physical Review C 106 (1), 014305, 2022
31 2022 Quantifying uncertainties on fission fragment mass yields with mixture density networks AE Lovell, AT Mohan, P Talou
Journal of Physics G: Nuclear and Particle Physics 47 (11), 114001, 2020
29 2020 Recent advances in the quantification of uncertainties in reaction theory AE Lovell, FM Nunes, M Catacora-Rios, GB King
Journal of Physics G: Nuclear and Particle Physics 48 (1), 014001, 2020
27 2020 Physically interpretable machine learning for nuclear masses MR Mumpower, TM Sprouse, AE Lovell, AT Mohan
Physical Review C 106 (2), L021301, 2022
26 2022 Exploring experimental conditions to reduce uncertainties in the optical potential M Catacora-Rios, GB King, AE Lovell, FM Nunes
Physical Review C 100 (6), 064615, 2019
25 2019 Informing nuclear physics via machine learning methods with differential and integral experiments D Neudecker, O Cabellos, AR Clark, MJ Grosskopf, W Haeck, ...
Physical Review C 104 (3), 034611, 2021
24 2021 Energy dependence of nonlocal optical potentials AE Lovell, PL Bacq, P Capel, FM Nunes, LJ Titus
Physical Review C 96 (5), 051601, 2017
24 2017 Three-body model for the two-neutron emission of AE Lovell, FM Nunes, IJ Thompson
Physical Review C 95 (3), 034605, 2017
24 2017 Angular Momentum Removal by Neutron and -Ray Emissions during Fission Fragment Decays I Stetcu, AE Lovell, P Talou, T Kawano, S Marin, SA Pozzi, A Bulgac
Physical Review Letters 127 (22), 222502, 2021
23 2021 Uncertainty quantification due to optical potentials in models for ( ) reactions GB King, AE Lovell, FM Nunes
Physical Review C 98 (4), 044623, 2018
23 2018 Exploration of the energy dependence of proton nonlocal optical potentials MI Jaghoub, AE Lovell, FM Nunes
Physical Review C 98 (2), 024609, 2018
23 2018