T. Giani

1.9k total citations · 1 hit paper
17 papers, 646 citations indexed

About

T. Giani is a scholar working on Nuclear and High Energy Physics, Surgery and Computer Networks and Communications. According to data from OpenAlex, T. Giani has authored 17 papers receiving a total of 646 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Nuclear and High Energy Physics, 1 paper in Surgery and 1 paper in Computer Networks and Communications. Recurrent topics in T. Giani's work include Particle physics theoretical and experimental studies (15 papers), Quantum Chromodynamics and Particle Interactions (12 papers) and High-Energy Particle Collisions Research (11 papers). T. Giani is often cited by papers focused on Particle physics theoretical and experimental studies (15 papers), Quantum Chromodynamics and Particle Interactions (12 papers) and High-Energy Particle Collisions Research (11 papers). T. Giani collaborates with scholars based in Netherlands, Italy and United Kingdom. T. Giani's co-authors include Juan Rojo, Stefano Forte, Luigi Del Debbio, Richard D. Ball, Juan Cruz–Martinez, Emanuele R. Nocera, Stefano Carrazza, Zahari Kassabov, Maria Ubiali and Michael Wilson and has published in prestigious journals such as Nature, Journal of High Energy Physics and Physical review. B..

In The Last Decade

T. Giani

16 papers receiving 642 citations

Hit Papers

The path to proton structure at 1% accuracy 2022 2026 2023 2024 2022 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The path to proton structure at 1% accuracy
    2022 233 citations Richard D. Ball, Stefano Carrazza et al. The European Physical Journal C profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
T. Giani Netherlands 14 561 36 28 27 22 17 646
Juan Cruz–Martinez Switzerland 13 630 1.1× 63 1.8× 59 2.1× 34 1.3× 40 1.8× 34 732
A. van Hameren Poland 18 1.2k 2.1× 43 1.2× 39 1.4× 60 2.2× 17 0.8× 53 1.2k
Zahari Kassabov United Kingdom 15 1.6k 2.8× 63 1.8× 46 1.6× 77 2.9× 29 1.3× 22 1.7k
Rob Verheyen United Kingdom 11 635 1.1× 91 2.5× 20 0.7× 64 2.4× 17 0.8× 20 686
T. J. Hobbs United States 17 849 1.5× 39 1.1× 13 0.5× 29 1.1× 43 2.0× 46 906
Luca Rottoli Switzerland 23 1.6k 2.9× 62 1.7× 33 1.2× 87 3.2× 26 1.2× 43 1.7k
Leandro Cieri Italy 15 1.1k 1.9× 68 1.9× 20 0.7× 72 2.7× 33 1.5× 32 1.1k
É. Pilon France 16 900 1.6× 36 1.0× 22 0.8× 77 2.9× 33 1.5× 30 928
Ryo Takahashi Japan 12 299 0.5× 22 0.6× 43 1.5× 166 6.1× 10 0.5× 65 443
C. Studerus Switzerland 9 721 1.3× 29 0.8× 20 0.7× 41 1.5× 20 0.9× 10 772

Countries citing papers authored by T. Giani

Since Specialization
Citations

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

Fields of papers citing papers by T. Giani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

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

All Works

17 of 17 papers shown
1.
Giani, T., et al.. (2024). Mapping the SMEFT at high-energy colliders: from LEP and the (HL-)LHC to the FCC-ee. Journal of High Energy Physics. 2024(9). 36 indexed citations
2.
3.
Ball, Richard D., Alessandro Candido, Juan Cruz–Martinez, et al.. (2024). Intrinsic charm quark valence distribution of the proton. Physical review. D. 109(9). 20 indexed citations
4.
Ball, Richard D., Alessandro Candido, Stefano Carrazza, et al.. (2024). Photons in the proton: implications for the LHC. The European Physical Journal C. 84(5). 13 indexed citations
5.
Cruz–Martinez, Juan, et al.. (2024). The LHC as a Neutrino-Ion Collider. The European Physical Journal C. 84(4). 16 indexed citations
6.
Candido, Alessandro, Stefano Forte, T. Giani, & Felix Hekhorn. (2024). On the positivity of $$\overline{\textrm{MS}}$$ parton distributions. The European Physical Journal C. 84(3). 6 indexed citations
7.
Ball, Richard D., Alessandro Candido, Stefano Carrazza, et al.. (2024). Determination of the theory uncertainties from missing higher orders on NNLO parton distributions with percent accuracy. The European Physical Journal C. 84(5). 18 indexed citations
8.
Candido, Alessandro, et al.. (2024). Bayesian inference with Gaussian processes for the determination of parton distribution functions. The European Physical Journal C. 84(7). 4 indexed citations
9.
Ball, Richard D., Stefano Carrazza, Juan Cruz–Martinez, et al.. (2022). The path to proton structure at 1% accuracy. The European Physical Journal C. 82(5). 233 indexed citations breakdown →
10.
Ball, Richard D., Alessandro Candido, Juan Cruz–Martinez, et al.. (2022). Evidence for intrinsic charm quarks in the proton. Nature. 608(7923). 483–487. 66 indexed citations
11.
Debbio, Luigi Del, T. Giani, & Michael G. Wilson. (2022). Bayesian approach to inverse problems: an application to NNPDF closure testing. The European Physical Journal C. 82(4). 38 indexed citations
12.
Ball, Richard D., Stefano Carrazza, Juan Cruz–Martinez, et al.. (2021). An open-source machine learning framework for global analyses of parton distributions. The European Physical Journal C. 81(10). 50 indexed citations
13.
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
14.
Cossu, Guido, Luigi Del Debbio, T. Giani, Ava Khamseh, & Michael Wilson. (2019). Machine learning determination of dynamical parameters: The Ising model case. Physical review. B.. 100(6). 20 indexed citations
15.
Khalek, Rabah Abdul, Richard D. Ball, Stefano Carrazza, et al.. (2019). Parton Distributions with Theory Uncertainties: General Formalism and First Phenomenological Studies. Edinburgh Research Explorer (University of Edinburgh). 57 indexed citations
16.
Forte, Stefano, T. Giani, & Davide Napoletano. (2019). Fitting the b-quark PDF as a massive-b scheme: Higgs production in bottom fusion. The European Physical Journal C. 79(7). 13 indexed citations
17.
Khalek, Rabah Abdul, Richard D. Ball, Stefano Carrazza, et al.. (2019). A first determination of parton distributions with theoretical uncertainties. Edinburgh Research Explorer (University of Edinburgh). 28 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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