V. Radescu

31.0k total citations · 1 hit paper
10 papers, 572 citations indexed

About

V. Radescu is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, V. Radescu has authored 10 papers receiving a total of 572 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Nuclear and High Energy Physics, 1 paper in Computer Networks and Communications and 1 paper in Artificial Intelligence. Recurrent topics in V. Radescu's work include Particle physics theoretical and experimental studies (8 papers), High-Energy Particle Collisions Research (7 papers) and Quantum Chromodynamics and Particle Interactions (7 papers). V. Radescu is often cited by papers focused on Particle physics theoretical and experimental studies (8 papers), High-Energy Particle Collisions Research (7 papers) and Quantum Chromodynamics and Particle Interactions (7 papers). V. Radescu collaborates with scholars based in Germany, United Kingdom and Switzerland. V. Radescu's co-authors include A. M. Cooper-Sarkar, A. Morsch, Juan Rojo, R. S. Thorne, Pavel Nadolsky, Jun Gao, Zahari Kassabov, R. McNulty, Stefano Carrazza and J. Huston and has published in prestigious journals such as Physics Letters B, Journal of High Energy Physics and Physical review. D.

In The Last Decade

V. Radescu

9 papers receiving 559 citations

Hit Papers

PDF4LHC recommendations for LHC Run II 2016 2026 2019 2022 2016 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
V. Radescu Germany 6 568 24 12 12 11 10 572
R. Plačakytė Germany 8 479 0.8× 12 0.5× 10 0.8× 10 0.8× 8 0.7× 14 489
R. McNulty Ireland 4 372 0.7× 19 0.8× 8 0.7× 10 0.8× 7 0.6× 9 380
Valentin Ahrens Germany 8 563 1.0× 30 1.3× 6 0.5× 5 0.4× 14 1.3× 9 567
Sasha Glazov Germany 3 378 0.7× 18 0.8× 8 0.7× 9 0.8× 7 0.6× 3 381
John M. Campbell United States 10 577 1.0× 36 1.5× 16 1.3× 20 1.7× 6 0.5× 21 583
Ben D. Pecjak Germany 17 956 1.7× 37 1.5× 6 0.5× 9 0.8× 7 0.6× 28 971
Joël Feltesse France 3 360 0.6× 17 0.7× 9 0.8× 9 0.8× 7 0.6× 6 363
Alba Soto-Ontoso France 13 346 0.6× 22 0.9× 15 1.3× 8 0.7× 4 0.4× 27 357
James Currie Switzerland 10 389 0.7× 18 0.8× 13 1.1× 11 0.9× 11 1.0× 13 405
J. Pires Switzerland 13 508 0.9× 30 1.3× 12 1.0× 11 0.9× 20 1.8× 21 529

Countries citing papers authored by V. Radescu

Since Specialization
Citations

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

Fields of papers citing papers by V. Radescu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of V. Radescu

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

All Works

10 of 10 papers shown
1.
Giachero, A., et al.. (2025). Enhanced feature encoding and classification on distributed quantum hardware. Machine Learning Science and Technology. 6(1). 15056–15056. 1 indexed citations
2.
Martínez, A. Bermúdez, Patrick Connor, H. Jung, et al.. (2019). Collinear and TMD parton densities from fits to precision DIS measurements in the parton branching method. Physical review. D. 99(7). 54 indexed citations
3.
Hautmann, F., H. Jung, A. Lelek, V. Radescu, & R. Žlebčík. (2017). Soft-gluon resolution scale in QCD evolution equations. Physics Letters B. 772. 446–451. 56 indexed citations
4.
Bertone, Valerio, D. Britzger, S. Camarda, et al.. (2017). Impact of the heavy-quark matching scales in PDF fits. The European Physical Journal C. 77(12). 837–837. 8 indexed citations
5.
Bertone, Valerio, S. Camarda, A. M. Cooper-Sarkar, et al.. (2016). A determination of m c (m c ) from HERA data using a matched heavy-flavor scheme. Journal of High Energy Physics. 2016(8). 4 indexed citations
6.
Butterworth, J. M., Stefano Carrazza, A. M. Cooper-Sarkar, et al.. (2016). PDF4LHC recommendations for LHC Run II. Journal of Physics G Nuclear and Particle Physics. 43(2). 23001–23001. 354 indexed citations breakdown →
7.
Borodin, M., et al.. (2015). Evolution of ATLAS conditions data and its management for LHC Run-2. Journal of Physics Conference Series. 664(4). 42005–42005. 1 indexed citations
8.
Camarda, S., P. Belov, A. M. Cooper-Sarkar, et al.. (2015). QCD analysis of W- and Z-boson production at Tevatron. The European Physical Journal C. 75(9). 20 indexed citations
9.
Radescu, V.. (2011). Parton Distributions from HERA. AIP conference proceedings. 37–42.
10.
Schienbein, I., V. Radescu, G. Zeller, et al.. (2008). Target mass corrections. Journal of Physics G Nuclear and Particle Physics. 35(5). 53101–53101. 74 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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