Giovanni Stagnitto

408 total citations
21 papers, 195 citations indexed

About

Giovanni Stagnitto is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Infectious Diseases. According to data from OpenAlex, Giovanni Stagnitto has authored 21 papers receiving a total of 195 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Nuclear and High Energy Physics, 1 paper in Artificial Intelligence and 0 papers in Infectious Diseases. Recurrent topics in Giovanni Stagnitto's work include Particle physics theoretical and experimental studies (21 papers), Quantum Chromodynamics and Particle Interactions (18 papers) and High-Energy Particle Collisions Research (17 papers). Giovanni Stagnitto is often cited by papers focused on Particle physics theoretical and experimental studies (21 papers), Quantum Chromodynamics and Particle Interactions (18 papers) and High-Energy Particle Collisions Research (17 papers). Giovanni Stagnitto collaborates with scholars based in Switzerland, Italy and United Kingdom. Giovanni Stagnitto's co-authors include T. Gehrmann, Alexander Huss, R. Gauld, A. Gehrmann–De Ridder, Matteo Cacciari, E. W. N. Glover, Stefano Frixione, Stefano Forte, Juan Rojo and Emanuele R. Nocera and has published in prestigious journals such as Physical Review Letters, Physics Letters B and Journal of High Energy Physics.

In The Last Decade

Giovanni Stagnitto

18 papers receiving 194 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Giovanni Stagnitto Switzerland 9 172 16 9 8 7 21 195
Д. Деркач Russia 6 134 0.8× 14 0.9× 4 0.4× 5 0.6× 6 0.9× 26 153
Mathias Ritzmann Switzerland 7 322 1.9× 19 1.2× 7 0.8× 4 0.5× 4 0.6× 8 335
Patrick Connor Germany 6 128 0.7× 20 1.3× 24 2.7× 6 0.8× 3 0.4× 17 169
C. Vernieri United States 5 252 1.5× 25 1.6× 21 2.3× 8 1.0× 7 1.0× 17 273
Y. Takubo Japan 6 77 0.4× 16 1.0× 28 3.1× 6 0.8× 11 1.6× 28 100
P. Urquijo Australia 5 149 0.9× 17 1.1× 5 0.6× 6 0.8× 8 1.1× 14 157
S. Fazio United States 8 215 1.3× 10 0.6× 14 1.6× 5 0.6× 6 0.9× 17 243
M. Narain United States 8 178 1.0× 10 0.6× 7 0.8× 7 0.9× 6 0.9× 20 188
J. M. Kalk United States 3 357 2.1× 10 0.6× 3 0.3× 8 1.0× 5 0.7× 3 375
R. Gran United States 6 146 0.8× 9 0.6× 6 0.7× 4 0.5× 8 1.1× 12 165

Countries citing papers authored by Giovanni Stagnitto

Since Specialization
Citations

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

Fields of papers citing papers by Giovanni Stagnitto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giovanni Stagnitto

This figure shows the co-authorship network connecting the top 25 collaborators of Giovanni Stagnitto. A scholar is included among the top collaborators of Giovanni Stagnitto 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 Giovanni Stagnitto. Giovanni Stagnitto 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.
Broggio, Alessandro, et al.. (2026). NNLO predictions with nonlocal subtractions and fiducial power corrections in GENEVA. Journal of High Energy Physics. 2026(1).
2.
Chen, Xuan, et al.. (2025). Jet production at electron-positron colliders at next-to-next-to-next-to-leading order in QCD. Physics Letters B. 869. 139804–139804.
3.
Gehrmann, T., et al.. (2025). Neutral and charged current semi-inclusive deep-inelastic scattering at NNLO QCD. Journal of High Energy Physics. 2025(10). 3 indexed citations
4.
Gehrmann, T., et al.. (2025). Identified Hadron Production in Deeply Inelastic Neutrino-Nucleon Scattering. Physical Review Letters. 135(21). 211902–211902.
5.
Ridder, A. Gehrmann–De, et al.. (2024). QCD predictions for vector boson plus hadron production at the LHC. Journal of High Energy Physics. 2024(10). 3 indexed citations
6.
Cacciari, Matteo, et al.. (2024). Heavy quark fragmentation in e+e− collisions to NNLO+NNLL accuracy in perturbative QCD. Journal of High Energy Physics. 2024(6). 7 indexed citations
7.
Gehrmann, T., et al.. (2024). Polarized Semi-Inclusive Deep-Inelastic Scattering at Next-to-Next-to-Leading Order in QCD. Physical Review Letters. 133(21). 211904–211904. 12 indexed citations
8.
Gehrmann, T., et al.. (2024). Antenna subtraction for processes with identified particles at hadron colliders. Journal of High Energy Physics. 2024(8). 11 indexed citations
9.
Ridder, A. Gehrmann–De, T. Gehrmann, E. W. N. Glover, et al.. (2024). Precise QCD predictions for W-boson production in association with a charm jet. The European Physical Journal C. 84(4). 361–361. 4 indexed citations
10.
Gehrmann, T., et al.. (2024). Semi-Inclusive Deep-Inelastic Scattering at Next-to-Next-to-Leading Order in QCD. Zurich Open Repository and Archive (University of Zurich). 21 indexed citations
11.
Frixione, Stefano & Giovanni Stagnitto. (2023). The muon parton distribution functions. Journal of High Energy Physics. 2023(12). 6 indexed citations
12.
Gauld, R., Alexander Huss, & Giovanni Stagnitto. (2023). Flavor Identification of Reconstructed Hadronic Jets. Physical Review Letters. 130(16). 161901–161901. 28 indexed citations
13.
Chen, Xuan, et al.. (2023). The parton-level structure of Higgs decays to hadrons at N3LO. Journal of High Energy Physics. 2023(6). 3 indexed citations
14.
Stagnitto, Giovanni, et al.. (2023). The parton-level structure of e+e− to 2 jets at N3LO. Journal of High Energy Physics. 2023(1). 14 indexed citations
15.
Chen, Xuan, et al.. (2023). Radiation from a gluon-gluino colour-singlet dipole at N3LO. Journal of High Energy Physics. 2023(12). 6 indexed citations
16.
Gauld, R., A. Gehrmann–De Ridder, E. W. N. Glover, et al.. (2023). NNLO QCD predictions for Z-boson production in association with a charm jet within the LHCb fiducial region. The European Physical Journal C. 83(4). 336–336. 13 indexed citations
17.
Bertone, Valerio, Matteo Cacciari, Stefano Frixione, et al.. (2022). Improving methods and predictions at high-energy e+e− colliders within collinear factorisation. Journal of High Energy Physics. 2022(10). 13 indexed citations
18.
Coccaro, A., Charanjit K. Khosa, Simone Marzani, et al.. (2021). Tagging the Higgs boson decay to bottom quarks with colour-sensitive observables and the Lund jet plane. arXiv (Cornell University). 7 indexed citations
19.
Khalek, Rabah Abdul, Stefano Forte, T. Gehrmann, et al.. (2020). Phenomenology of NNLO jet production at the LHC and its impact on parton distributions. Zurich Open Repository and Archive (University of Zurich). 28 indexed citations
20.
Cacciari, Matteo, Stefano Forte, Davide Napoletano, Grégory Soyez, & Giovanni Stagnitto. (2019). Single-jet inclusive cross section and its definition. Physical review. D. 100(11). 6 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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