Theo Heimel

473 total citations
9 papers, 192 citations indexed

About

Theo Heimel is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Theo Heimel has authored 9 papers receiving a total of 192 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Nuclear and High Energy Physics, 3 papers in Artificial Intelligence and 2 papers in Computer Networks and Communications. Recurrent topics in Theo Heimel's work include Particle physics theoretical and experimental studies (9 papers), High-Energy Particle Collisions Research (4 papers) and Computational Physics and Python Applications (3 papers). Theo Heimel is often cited by papers focused on Particle physics theoretical and experimental studies (9 papers), High-Energy Particle Collisions Research (4 papers) and Computational Physics and Python Applications (3 papers). Theo Heimel collaborates with scholars based in Germany, United States and Belgium. Theo Heimel's co-authors include Tilman Plehn, Anja Butter, Ramon Winterhalder, Claudius Krause, Olivier Mattelaer, Till Martini, Tobias Krebs, David Shih, Rahool Kumar Barman and Joshua Isaacson and has published in prestigious journals such as SciPost Physics.

In The Last Decade

Theo Heimel

9 papers receiving 192 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Theo Heimel Germany	8	152	58	16	15	14	9	192
Ramon Winterhalder Germany	7	185 1.2×	73 1.3×	17 1.1×	13 0.9×	12 0.9×	11	233
S.‐C. Hsu United States	7	167 1.1×	64 1.1×	11 0.7×	12 0.8×	6 0.4×	33	229
Engin Eren Germany	7	210 1.4×	80 1.4×	25 1.6×	11 0.7×	14 1.0×	11	266
Sascha Diefenbacher Germany	10	252 1.7×	90 1.6×	28 1.8×	12 0.8×	20 1.4×	16	320
J. A. Raine Switzerland	10	142 0.9×	59 1.0×	6 0.4×	6 0.4×	10 0.7×	19	179
Erik Buhmann Germany	9	227 1.5×	95 1.6×	24 1.5×	9 0.6×	18 1.3×	12	290
L. Gouskos Switzerland	3	157 1.0×	58 1.0×	7 0.4×	10 0.7×	6 0.4×	5	197
T. Golling Switzerland	13	336 2.2×	90 1.6×	9 0.6×	18 1.2×	10 0.7×	34	388
Dylan Rankin United States	8	81 0.5×	76 1.3×	8 0.5×	32 2.1×	9 0.6×	16	200
Olmo Cerri United States	7	103 0.7×	67 1.2×	4 0.3×	13 0.9×	3 0.2×	9	162

Countries citing papers authored by Theo Heimel

Since Specialization

Citations

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

Fields of papers citing papers by Theo Heimel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Theo Heimel

This figure shows the co-authorship network connecting the top 25 collaborators of Theo Heimel. A scholar is included among the top collaborators of Theo Heimel 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 Theo Heimel. Theo Heimel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Heimel, Theo, et al.. (2025). Differentiable MadNIS-Lite. SciPost Physics. 18(1). 5 indexed citations

2.

Shmakov, Alexander, Sascha Diefenbacher, V. M. Mikuni, et al.. (2025). The landscape of unfolding with machine learning. SciPost Physics. 18(2). 11 indexed citations

3.

Heimel, Theo, et al.. (2024). The MadNIS reloaded. SciPost Physics. 17(1). 17 indexed citations

4.

Heimel, Theo, et al.. (2024). Precision-machine learning for the matrix element method. SciPost Physics. 17(5). 15 indexed citations

5.

Barman, Rahool Kumar, et al.. (2024). Returning CP-observables to the frames they belong. SciPost Physics. 17(1). 18 indexed citations

6.

Heimel, Theo, et al.. (2024). How to understand limitations of generative networks. SciPost Physics. 16(1). 28 indexed citations

7.

Butter, Anja, et al.. (2023). Two invertible networks for the matrix element method. SciPost Physics. 15(3). 28 indexed citations

8.

Butter, Anja, et al.. (2023). Generative networks for precision enthusiasts. SciPost Physics. 14(4). 40 indexed citations

9.

Heimel, Theo, Ramon Winterhalder, Anja Butter, et al.. (2023). MadNIS - Neural multi-channel importance sampling. SciPost Physics. 15(4). 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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