P. Gras

3.6k citations
8 papers · 103 indexed · h-index 4

Impact in

    • Particle physics theoretical and experimental studies
    • High-Energy Particle Collisions Research
    • Quantum Chromodynamics and Particle Interactions
    • Particle Detector Development and Performance
    • Dark Matter and Cosmic Phenomena
    • Black Holes and Theoretical Physics

Papers in

P. Gras

6 papers receiving 96 citations

Peers

P. Gras
Comparison fields: 5 of 22
  • Nuclear and High Energy Physics 91
  • Hardware and Architecture 4
  • Radiation 3
  • Artificial Intelligence 10
  • Software 1
Replace K. Cho with:
K. Cho South Korea
A. Sapronov Russia
P. Musella Switzerland
Alexander Spiridonov Russia
S. Pagan Griso United States
Y. Sakamoto Japan
P. Wittich United States
A. Coccaro Italy
Justin Pilot Germany
Y. Coadou France
P. Gras relative to K. Cho South Korea K. Cho's profile →
Citations per field
00.5×8.5×
K. Cho · 1×
Citations per year

Countries citing papers authored by P. Gras

Since Specialization
Citations

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

Fields of papers citing papers by P. Gras

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside P. Gras, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with P. Gras Line = papers co-authored together P. Gras links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 201785
2 20237
3 20024
4
Results of the OPC Evaluation Done within JCOP for the Control of the LHC Experiments
19994
5 20241
6
FRONT-END ELECTRONICS CONFIGURATION SYSTEM FOR CMS
20011
7 20151
8 20250

About P. Gras

P. Gras is a scholar working on Nuclear and High Energy Physics, Hardware and Architecture, Computer Networks and Communications, Radiation and Artificial Intelligence, having authored 8 papers that have together received 103 indexed citations. Recurring topics across this work include Particle Detector Development and Performance (5 papers), Advanced Data Storage Technologies (3 papers), Distributed and Parallel Computing Systems (3 papers), Parallel Computing and Optimization Techniques (2 papers), Computational Physics and Python Applications (2 papers), Particle physics theoretical and experimental studies (2 papers), Quantum Chromodynamics and Particle Interactions (1 paper) and Radiation Detection and Scintillator Technologies (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (91 citations), Hardware and Architecture (4 citations), Radiation (3 citations), Artificial Intelligence (10 citations) and Software (1 citation). P. Gras has collaborated with scholars based in France, Switzerland and United Kingdom. Frequent co-authors include Simon Plätzer, Stefan Höche, D. Kar, Jesse Thaler, Leif Lönnblad, Andrew J. Larkoski, Andrzej Siódmok, Peter Skands, Grégory Soyez and Benedikt Hegner. Their work appears in journals such as Journal of High Energy Physics, IEEE Transactions on Nuclear Science, SHILAP Revista de lepidopterología, Journal of Physics Conference Series and EPJ Web of Conferences.

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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