Michael Spannowsky

6.3k total citations
137 papers, 3.9k citations indexed

About

Michael Spannowsky is a scholar working on Nuclear and High Energy Physics, Astronomy and Astrophysics and Artificial Intelligence. According to data from OpenAlex, Michael Spannowsky has authored 137 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 109 papers in Nuclear and High Energy Physics, 32 papers in Astronomy and Astrophysics and 31 papers in Artificial Intelligence. Recurrent topics in Michael Spannowsky's work include Particle physics theoretical and experimental studies (105 papers), High-Energy Particle Collisions Research (51 papers) and Cosmology and Gravitation Theories (32 papers). Michael Spannowsky is often cited by papers focused on Particle physics theoretical and experimental studies (105 papers), High-Energy Particle Collisions Research (51 papers) and Cosmology and Gravitation Theories (32 papers). Michael Spannowsky collaborates with scholars based in United Kingdom, Germany and United States. Michael Spannowsky's co-authors include Christoph Englert, Tilman Plehn, Joerg Jaeckel, Davison E. Soper, Matthew J. Dolan, Graham D. Kribs, Tim M. P. Tait, Gavin P. Salam, Michihisa Takeuchi and Martin Jankowiak and has published in prestigious journals such as Physical Review Letters, Scientific Reports and Nuclear Physics B.

In The Last Decade

Michael Spannowsky

132 papers receiving 3.9k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Michael Spannowsky 3.4k 788 576 238 98 137 3.9k
Valentin Hirschi 4.7k 1.4× 878 1.1× 284 0.5× 76 0.3× 63 0.6× 31 4.8k
Olivier Mattelaer 5.5k 1.6× 1.1k 1.4× 369 0.6× 83 0.3× 52 0.5× 47 5.6k
Tilman Plehn 7.1k 2.1× 1.7k 2.2× 570 1.0× 112 0.5× 135 1.4× 171 7.3k
Rikkert Frederix 5.8k 1.7× 984 1.2× 326 0.6× 80 0.3× 46 0.5× 46 5.9k
Paolo Torrielli 4.4k 1.3× 799 1.0× 274 0.5× 69 0.3× 39 0.4× 35 4.5k
Stefano Frixione 8.2k 2.4× 1.1k 1.4× 357 0.6× 119 0.5× 134 1.4× 68 8.3k
Juan Rojo 6.7k 1.9× 439 0.6× 242 0.4× 110 0.5× 74 0.8× 124 6.9k
E. W. N. Glover 7.0k 2.0× 598 0.8× 196 0.3× 122 0.5× 119 1.2× 184 7.2k
M. Czakon 6.5k 1.9× 800 1.0× 157 0.3× 127 0.5× 142 1.4× 113 6.7k
S. Mrenna 9.5k 2.8× 1.7k 2.2× 490 0.9× 117 0.5× 83 0.8× 60 9.7k

Countries citing papers authored by Michael Spannowsky

Since Specialization
Citations

This map shows the geographic impact of Michael Spannowsky'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 Michael Spannowsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Spannowsky more than expected).

Fields of papers citing papers by Michael Spannowsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Spannowsky. 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 Michael Spannowsky. The network helps show where Michael Spannowsky may publish in the future.

Co-authorship network of co-authors of Michael Spannowsky

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Spannowsky. A scholar is included among the top collaborators of Michael Spannowsky 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 Michael Spannowsky. Michael Spannowsky 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.
Spannowsky, Michael, et al.. (2025). Real-time scattering processes with continuous-variable quantum computers. Physical review. A. 112(1).
2.
Sakurai, Kazuki, et al.. (2025). Enhancing anomaly detection with topology-aware autoencoders. Machine Learning Science and Technology. 6(4). 45051–45051.
3.
Maître, D., et al.. (2025). Optimal equivariant architectures from the symmetries of matrix-element likelihoods. Machine Learning Science and Technology. 6(1). 15059–15059. 3 indexed citations
4.
Brown, Christopher Edward, et al.. (2024). Quantum pathways for charged track finding in high-energy collisions. Frontiers in Artificial Intelligence. 7. 1339785–1339785. 3 indexed citations
5.
Philipsen, Owe, et al.. (2024). Simulating $Z_{2}$ lattice gauge theory with the variational quantum thermalizer. EPJ Quantum Technology. 11(1). 7 indexed citations
6.
Spannowsky, Michael, et al.. (2024). Interpretable deep learning models for the inference and classification of LHC data. Journal of High Energy Physics. 2024(5). 3 indexed citations
7.
Criado, Juan Carlos, et al.. (2024). Charting the free energy landscape of metastable topological magnetic objects. Physical review. B.. 109(19). 1 indexed citations
8.
Sakurai, Kazuki & Michael Spannowsky. (2024). Three-Body Entanglement in Particle Decays. Physical Review Letters. 132(15). 151602–151602. 13 indexed citations
9.
Konar, Partha, et al.. (2024). Hypergraphs in LHC phenomenology — the next frontier of IRC-safe feature extraction. Journal of High Energy Physics. 2024(1). 6 indexed citations
10.
Heurtier, Lucien, et al.. (2023). Hunting for neutral leptons with ultrahigh-energy neutrinos. Physical review. D. 108(5). 2 indexed citations
11.
Criado, Juan Carlos, Michael Spannowsky, & R. Kogler. (2023). Quantum fitting framework applied to effective field theories. Physical review. D. 107(1). 4 indexed citations
12.
Chakrabortty, Joydeep, et al.. (2022). Landscaping CP-violating BSM scenarios. Durham Research Online (Durham University). 15 indexed citations
13.
Abel, Steven, Juan Carlos Criado, & Michael Spannowsky. (2022). Completely quantum neural networks. Physical review. A. 106(2). 21 indexed citations
14.
Spannowsky, Michael, et al.. (2022). Quantum walk approach to simulating parton showers. Physical review. D. 106(5). 27 indexed citations
15.
Criado, Juan Carlos & Michael Spannowsky. (2022). Qade: solving differential equations on quantum annealers. Quantum Science and Technology. 8(1). 15021–15021. 9 indexed citations
16.
Konar, Partha, et al.. (2022). Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm. Journal of High Energy Physics. 2022(2). 25 indexed citations
17.
Chakrabortty, Joydeep, et al.. (2021). CP violation at ATLAS in effective field theory. Physical review. D. 103(5). 12 indexed citations
18.
Chakrabortty, Joydeep, et al.. (2021). Extended Higgs boson sectors, effective field theory, and Higgs boson phenomenology. Physical review. D. 103(9). 5 indexed citations
19.
Balunas, W. K., Michael Spannowsky, B. Stanislaus, et al.. (2020). Higgs self-coupling measurements using deep learning in the $ b\overline{b}b\overline{b} $ final state. DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron). 9 indexed citations
20.
Bernlochner, F. U., Christoph Englert, C. P. Hays, et al.. (2019). Angles on CP-violation in Higgs boson interactions. Physics Letters B. 790. 372–379. 30 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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