T. Golling

119.9k total citations
34 papers, 388 citations indexed

About

T. Golling is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, T. Golling has authored 34 papers receiving a total of 388 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Nuclear and High Energy Physics, 9 papers in Artificial Intelligence and 4 papers in Computer Networks and Communications. Recurrent topics in T. Golling's work include Particle physics theoretical and experimental studies (25 papers), Particle Detector Development and Performance (15 papers) and High-Energy Particle Collisions Research (6 papers). T. Golling is often cited by papers focused on Particle physics theoretical and experimental studies (25 papers), Particle Detector Development and Performance (15 papers) and High-Energy Particle Collisions Research (6 papers). T. Golling collaborates with scholars based in Switzerland, United States and France. T. Golling's co-authors include J. A. Raine, Matthew Leigh, K. Zoch, S. B. Klein, G. Quétant, Benjamin Nachman, D. Sengupta, Cédric Delaunay, Yotam Soreq and Gilad Perez and has published in prestigious journals such as SHILAP Revista de lepidopterología, Monthly Notices of the Royal Astronomical Society and Journal of High Energy Physics.

In The Last Decade

T. Golling

27 papers receiving 384 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
T. Golling Switzerland	13	336	90	58	22	19	34	388
Rob Verheyen United Kingdom	11	635 1.9×	91 1.0×	64 1.1×	12 0.5×	12 0.6×	20	686
Philip Harris United States	9	226 0.7×	106 1.2×	39 0.7×	6 0.3×	40 2.1×	21	357
Sascha Diefenbacher Germany	10	252 0.8×	90 1.0×	22 0.4×	22 1.0×	39 2.1×	16	320
S.‐C. Hsu United States	7	167 0.5×	64 0.7×	18 0.3×	13 0.6×	12 0.6×	33	229
Joshua Isaacson United States	12	438 1.3×	116 1.3×	21 0.4×	10 0.5×	16 0.8×	32	515
Andrzej Siódmok Poland	14	576 1.7×	45 0.5×	47 0.8×	8 0.4×	9 0.5×	42	618
Christina Gao United States	8	230 0.7×	71 0.8×	62 1.1×	6 0.3×	6 0.3×	16	295
Ramon Winterhalder Germany	7	185 0.6×	73 0.8×	18 0.3×	8 0.4×	16 0.8×	11	233
Dylan Rankin United States	8	81 0.2×	76 0.8×	28 0.5×	16 0.7×	39 2.1×	16	200
H. Qu China	6	273 0.8×	108 1.2×	13 0.2×	9 0.4×	21 1.1×	15	348

Countries citing papers authored by T. Golling

Since Specialization

Citations

This map shows the geographic impact of T. Golling'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 T. Golling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Golling more than expected).

Fields of papers citing papers by T. Golling

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T. Golling

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

All Works

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20 of 20 papers shown

Leigh, Matthew, S. B. Klein, T. Golling, et al.. (2025). Is tokenization needed for masked particle modeling?. Machine Learning Science and Technology. 6(2). 25075–25075. 1 indexed citations

Raine, J. A., et al.. (2024). Decorrelation using optimal transport. The European Physical Journal C. 84(6). 579–579.

Leigh, Matthew, D. Sengupta, J. A. Raine, G. Quétant, & T. Golling. (2024). Faster diffusion model with improved quality for particle cloud generation. Physical review. D. 109(1). 23 indexed citations

Sengupta, D., et al.. (2024). CURTAINs flows for flows: Constructing unobserved regions with maximum likelihood estimation. SciPost Physics. 17(2). 15 indexed citations

Raine, J. A., Matthew Leigh, K. Zoch, & T. Golling. (2024). Fast and improved neutrino reconstruction in multineutrino final states with conditional normalizing flows. Physical review. D. 109(1). 13 indexed citations

Golling, T., Gregor Kasieczka, Claudius Krause, et al.. (2024). The interplay of machine learning-based resonant anomaly detection methods. The European Physical Journal C. 84(3). 241–241. 14 indexed citations

Leigh, Matthew, et al.. (2024). PC-JeDi: Diffusion for particle cloud generation in high energy physics. SciPost Physics. 16(1). 31 indexed citations

Leigh, Matthew, et al.. (2024). Improving new physics searches with diffusion models for event observables and jet constituents. Journal of High Energy Physics. 2024(4). 9 indexed citations

Quétant, G., J. A. Raine, Matthew Leigh, D. Sengupta, & T. Golling. (2024). Generating variable length full events from partons. Physical review. D. 110(7). 4 indexed citations

10.

Arguin, J-F., et al.. (2023). Variational autoencoders for anomalous jet tagging. Physical review. D. 107(1). 30 indexed citations

11.

Golling, T., et al.. (2023). Flow-enhanced transportation for anomaly detection. Physical review. D. 107(9). 28 indexed citations

12.

Ehrke, L. F., J. A. Raine, K. Zoch, M. Guth, & T. Golling. (2023). Topological reconstruction of particle physics processes using graph neural networks. Physical review. D. 107(11). 11 indexed citations

13.

Golling, T., et al.. (2023). Morphing one dataset into another with maximum likelihood estimation. Physical review. D. 108(9). 6 indexed citations

14.

Leigh, Matthew, J. A. Raine, K. Zoch, & T. Golling. (2023). $\nu$-flows: Conditional neutrino regression. SciPost Physics. 14(6). 22 indexed citations

15.

Kiehn, M., P. Calafiura, Steven Farrell, et al.. (2019). The TrackML high-energy physics tracking challenge on Kaggle. SHILAP Revista de lepidopterología. 214. 6037–6037. 4 indexed citations

16.

Golling, T.. (2016). LHC searches for exotic new particles. Progress in Particle and Nuclear Physics. 90. 156–200. 8 indexed citations

17.

Cohen, Timothy, T. Golling, A. Henrichs, et al.. (2014). SUSY simplified models at 14, 33, and 100 TeV proton colliders. Journal of High Energy Physics. 2014(4). 58 indexed citations

18.

Delaunay, Cédric, T. Golling, Gilad Perez, & Yotam Soreq. (2014). Enhanced Higgs boson coupling to charm pairs. Physical review. D. Particles, fields, gravitation, and cosmology. 89(3). 33 indexed citations

19.

Golling, T., et al.. (2008). Commissioning of the ATLAS pixel detector. University of North Texas Digital Library (University of North Texas). 1 indexed citations

20.

Golling, T.. (2002). SEARCH FOR ODDERON INDUCED CONTRIBUTIONS TO EXCLUSIVE π⁰ PHOTOPRODUCTION AT HERA. 732–737. 1 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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