A. E. Lovell

958 total citations
53 papers, 579 citations indexed

About

A. E. Lovell is a scholar working on Nuclear and High Energy Physics, Radiation and Aerospace Engineering. According to data from OpenAlex, A. E. Lovell has authored 53 papers receiving a total of 579 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Nuclear and High Energy Physics, 36 papers in Radiation and 34 papers in Aerospace Engineering. Recurrent topics in A. E. Lovell's work include Nuclear physics research studies (38 papers), Nuclear Physics and Applications (35 papers) and Nuclear reactor physics and engineering (33 papers). A. E. Lovell is often cited by papers focused on Nuclear physics research studies (38 papers), Nuclear Physics and Applications (35 papers) and Nuclear reactor physics and engineering (33 papers). A. E. Lovell collaborates with scholars based in United States, France and Austria. A. E. Lovell's co-authors include F. M. Nunes, P. Talou, Arvind Mohan, Matthew R. Mumpower, Toshihiko Kawano, Ionel Stetcu, Léo Neufcourt, F. M. Nunes, T. M. Sprouse and Shin Okumura and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Water Resources Research.

In The Last Decade

A. E. Lovell

47 papers receiving 566 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
A. E. Lovell United States 16 418 299 291 89 76 53 579
S. Stave United States 12 306 0.7× 82 0.3× 218 0.7× 128 1.4× 57 0.8× 40 459
M. Barbui United States 15 446 1.1× 124 0.4× 234 0.8× 175 2.0× 36 0.5× 59 590
R. Nolte Germany 20 381 0.9× 379 1.3× 743 2.6× 132 1.5× 115 1.5× 95 1.0k
F. Gunsing France 14 308 0.7× 254 0.8× 405 1.4× 99 1.1× 58 0.8× 41 542
A. Zimbal Germany 15 238 0.6× 191 0.6× 401 1.4× 106 1.2× 138 1.8× 49 531
A. Chbihi France 15 491 1.2× 128 0.4× 159 0.5× 165 1.9× 36 0.5× 58 592
F. Giacoppo Norway 12 354 0.8× 74 0.2× 152 0.5× 107 1.2× 11 0.1× 34 447
Qingbiao Shen China 16 667 1.6× 217 0.7× 244 0.8× 218 2.4× 16 0.2× 63 740
E. Migneco Italy 12 402 1.0× 140 0.5× 207 0.7× 91 1.0× 30 0.4× 59 497
D. Henzlová United States 12 283 0.7× 241 0.8× 328 1.1× 63 0.7× 26 0.3× 43 483

Countries citing papers authored by A. E. Lovell

Since Specialization
Citations

This map shows the geographic impact of A. E. Lovell's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by A. E. Lovell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. E. Lovell more than expected).

Fields of papers citing papers by A. E. Lovell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. E. Lovell. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by A. E. Lovell. The network helps show where A. E. Lovell may publish in the future.

Co-authorship network of co-authors of A. E. Lovell

This figure shows the co-authorship network connecting the top 25 collaborators of A. E. Lovell. A scholar is included among the top collaborators of A. E. Lovell based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with A. E. Lovell. A. E. Lovell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Neudecker, Denise, Theresa Cutler, M. Devlin, et al.. (2025). Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement. Physical Review X. 15(2).
2.
Mosby, S., et al.. (2024). A high-intensity, low-energy heavy ion source for a neutron target proof-of-principle experiment at LANSCE. Journal of Physics Conference Series. 2743(1). 12091–12091.
3.
Pruitt, C. D., et al.. (2024). Role of the likelihood for elastic scattering uncertainty quantification. Physical review. C. 110(6). 1 indexed citations
4.
Neudecker, Denise, A.D. Carlson, S. Croft, et al.. (2023). Templates of expected measurement uncertainties for average prompt and total fission neutron multiplicities. SHILAP Revista de lepidopterología. 9. 30–30. 4 indexed citations
5.
Mumpower, Matthew R., T. M. Sprouse, Toshihiko Kawano, et al.. (2023). Nuclear data activities for medium mass and heavy nuclei at Los Alamos. SHILAP Revista de lepidopterología. 284. 12001–12001. 1 indexed citations
6.
Kelly, Keegan, M. Devlin, J. M. O’Donnell, et al.. (2023). Measurement of the U238(n,f) prompt fission neutron spectrum from 10 keV to 10 MeV induced by neutrons with 1.5–20 MeV energy. Physical review. C. 108(2). 2 indexed citations
7.
Mumpower, Matthew R., Denise Neudecker, Hirokazu Sasaki, et al.. (2023). Collective enhancement in the exciton model. Physical review. C. 107(3). 2 indexed citations
8.
Couture, A., E. M. Bond, T. A. Bredeweg, et al.. (2023). Measurement of the neutron-induced capture-to-fission cross section ratio in U233 at LANSCE. Physical review. C. 108(1). 2 indexed citations
9.
Kelly, Keegan, M. Devlin, J. M. O’Donnell, et al.. (2023). Correlations between energy and γ-ray emission in Pu239(n,f). Physical review. C. 107(1). 3 indexed citations
10.
Lovell, A. E., Toshihiko Kawano, & P. Talou. (2023). Calculated covariance matrices for fission product yields using BeoH. SHILAP Revista de lepidopterología. 281. 18–18. 3 indexed citations
11.
Lovell, A. E., Toshihiko Kawano, Shin Okumura, et al.. (2023). The Extension of the Hauser-Feshbach Fission Fragment Decay Model to Multi-chance Fission and its Application to 239Pu. SHILAP Revista de lepidopterología. 284. 4015–4015.
12.
Mumpower, Matthew R., et al.. (2023). Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach. Frontiers in Physics. 11. 5 indexed citations
13.
Kelly, Keegan, M. Devlin, J. M. O’Donnell, et al.. (2022). Measurement of the U235(n,f) prompt fission neutron spectrum from 10 keV to 10 MeV induced by neutrons of energy from 1 MeV to 20 MeV. Physical review. C. 105(4). 14 indexed citations
14.
Vogt, R., J. Randrup, P. Talou, et al.. (2021). Structure in the event-by-event energy-dependent neutron-γ multiplicity correlations in Cf252(sf). Physical review. C. 104(2). 6 indexed citations
15.
Neudecker, Denise, Ó. Cabellos, Michael Grosskopf, et al.. (2021). Informing nuclear physics via machine learning methods with differential and integral experiments. Physical review. C. 104(3). 20 indexed citations
16.
Talou, P., Ionel Stetcu, P. Jaffke, et al.. (2021). Fission fragment decay simulations with the CGMF code. Computer Physics Communications. 269. 108087–108087. 43 indexed citations
17.
Lovell, A. E., Arvind Mohan, & P. Talou. (2020). Quantifying uncertainties on fission fragment mass yields with mixture density networks. Journal of Physics G Nuclear and Particle Physics. 47(11). 114001–114001. 29 indexed citations
18.
Lovell, A. E., et al.. (2019). Direct Comparison between Bayesian and Frequentist Uncertainty Quantification for Nuclear Reactions. Physical Review Letters. 122(23). 232502–232502. 51 indexed citations
19.
Kelly, Keegan, Toshihiko Kawano, J. M. O’Donnell, et al.. (2019). Preequilibrium Asymmetries in the Pu239(n,f) Prompt Fission Neutron Spectrum. Physical Review Letters. 122(7). 72503–72503. 25 indexed citations
20.
Lovell, A. E., et al.. (2018). Uncertainty quantification due to optical potentials in models for (d,p) reactions. Physical review. C. 98(4). 17 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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