Д. Деркач

34.8k total citations
26 papers, 153 citations indexed

About

Д. Деркач is a scholar working on Nuclear and High Energy Physics, Radiation and Artificial Intelligence. According to data from OpenAlex, Д. Деркач has authored 26 papers receiving a total of 153 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Nuclear and High Energy Physics, 3 papers in Radiation and 2 papers in Artificial Intelligence. Recurrent topics in Д. Деркач's work include Particle physics theoretical and experimental studies (23 papers), High-Energy Particle Collisions Research (12 papers) and Particle Detector Development and Performance (11 papers). Д. Деркач is often cited by papers focused on Particle physics theoretical and experimental studies (23 papers), High-Energy Particle Collisions Research (12 papers) and Particle Detector Development and Performance (11 papers). Д. Деркач collaborates with scholars based in Russia, Italy and United Kingdom. Д. Деркач's co-authors include V. Lubicz, V. Vagnoni, M. Pierini, M. Ciuchini, M. Bóna, G. Martinelli, L. Silvestrini, E. Franco, F. Ratnikov and Cecilia Tarantino and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of High Energy Physics.

In The Last Decade

Д. Деркач

21 papers receiving 147 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Д. Деркач Russia 6 134 14 11 11 9 26 153
D. Rocco United States 4 90 0.7× 10 0.7× 17 1.5× 5 0.5× 17 1.9× 5 117
P. Vahle United States 3 78 0.6× 9 0.6× 17 1.5× 5 0.5× 17 1.9× 6 105
E. Niner United States 3 84 0.6× 8 0.6× 17 1.5× 5 0.5× 19 2.1× 5 110
A. Radovic Austria 2 75 0.6× 8 0.6× 17 1.5× 5 0.5× 19 2.1× 4 101
F. Psihas United States 3 80 0.6× 8 0.6× 18 1.6× 6 0.5× 22 2.4× 4 108
G. Pawloski United States 2 72 0.5× 7 0.5× 17 1.5× 5 0.5× 17 1.9× 4 98
R. Aaij United Kingdom 2 207 1.5× 12 0.9× 4 0.4× 7 0.6× 14 1.6× 2 215
M. Feickert United States 4 93 0.7× 10 0.7× 28 2.5× 5 0.5× 5 0.6× 13 119
M. Kado Italy 6 130 1.0× 27 1.9× 21 1.9× 8 0.7× 3 0.3× 9 137
I. Vivarelli Italy 5 147 1.1× 28 2.0× 7 0.6× 6 0.5× 25 2.8× 15 165

Countries citing papers authored by Д. Деркач

Since Specialization
Citations

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

Fields of papers citing papers by Д. Деркач

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Д. Деркач

This figure shows the co-authorship network connecting the top 25 collaborators of Д. Деркач. A scholar is included among the top collaborators of Д. Деркач 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 Д. Деркач. Д. Деркач 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.
Деркач, Д., et al.. (2025). Polarization Drift Compensation for Quantum Key Distribution Using Machine Learning. Physics of Particles and Nuclei. 56(6). 1503–1508.
2.
Valli, Mauro, M. Bóna, M. Ciuchini, et al.. (2024). Overview and theoretical prospects for CKM matrix and CP violation from the UTfit Collaboration. CERN Document Server (European Organization for Nuclear Research). 7–7. 2 indexed citations
3.
Anderlini, L., M. Barbetti, Д. Деркач, et al.. (2023). Towards Reliable Neural Generative Modeling of Detectors. Journal of Physics Conference Series. 2438(1). 12130–12130. 2 indexed citations
4.
Ryzhikov, A., et al.. (2023). Robust Neural Particle Identification Models. Journal of Physics Conference Series. 2438(1). 12119–12119.
5.
Anderlini, L., M. Barbetti, G. Corti, et al.. (2022). Lamarr: the ultra-fast simulation option for the LHCb experiment. Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022). 233–233. 1 indexed citations
6.
Bóna, M., M. Ciuchini, Д. Деркач, et al.. (2022). Unitarity Triangle global fits testing the Standard Model: UTfit 2021 Standard Model update. CERN Document Server (European Organization for Nuclear Research). 512–512. 2 indexed citations
7.
Boldyrev, A. S., Д. Деркач, F. Ratnikov, & Alexey Shevelev. (2021). Machine Learning in Calorimeter optimization. Journal of Physics Conference Series. 1740. 12047–12047. 3 indexed citations
8.
Деркач, Д., M. Hushchyn, & N. Kazeev. (2019). Machine Learning based Global Particle Identification Algorithms at the LHCb Experiment. SHILAP Revista de lepidopterología. 214. 6011–6011. 4 indexed citations
9.
Деркач, Д., et al.. (2019). Cherenkov detectors fast simulation using neural networks. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 952. 161804–161804. 16 indexed citations
10.
Деркач, Д., M. Hushchyn, Tatiana Likhomanenko, et al.. (2018). Machine-Learning-based global particle-identification algorithms at the LHCb experiment. Journal of Physics Conference Series. 1085. 42038–42038. 3 indexed citations
11.
Bóna, M., C. Alpigiani, A. J. Bevan, et al.. (2017). Unitarity Triangle Analysis and D meson mixing in the Standard Model and Beyond. 205–205. 4 indexed citations
12.
Bóna, M., C. Alpigiani, A. J. Bevan, et al.. (2017). Unitarity Triangle analysis in the Standard Model from the UTfit collaboration. 554–554. 5 indexed citations
13.
Kelbauskas, Laimonas, Shashaanka Ashili, Jia Zeng, et al.. (2017). Platform for combined analysis of functional and biomolecular phenotypes of the same cell. Scientific Reports. 7(1). 44636–44636. 3 indexed citations
14.
Bóna, M., C. Alpigiani, A. J. Bevan, et al.. (2017). Neutral charm mixing results from the Utfit collaboration. 143–143. 2 indexed citations
15.
Baranov, A., Evgeny Burnaev, Д. Деркач, et al.. (2017). Optimising the Active Muon Shield for the SHiP Experiment at CERN. Journal of Physics Conference Series. 934. 12050–12050. 4 indexed citations
16.
Adinolfi, M., F. Archilli, W. Baldini, et al.. (2017). LHCb data quality monitoring. Journal of Physics Conference Series. 898. 92027–92027. 4 indexed citations
17.
Bevan, A. J., M. Bóna, M. Ciuchini, et al.. (2014). The UTfit collaboration average of D meson mixing data: Winter 2014. Journal of High Energy Physics. 2014(3). 18 indexed citations
18.
Bevan, A. J., M. Bóna, M. Ciuchini, et al.. (2013). Standard Model updates and new physics analysis with the Unitarity Triangle fit. Nuclear Physics B - Proceedings Supplements. 241-242. 89–94. 14 indexed citations
19.
Деркач, Д.. (2012). Direct and mixing-induced CP violation in charmless two-body B decays.. 36–36. 1 indexed citations
20.
Bevan, A. J., M. Bóna, M. Ciuchini, et al.. (2010). Unitarity Triangle Analysis: An Update. Nuclear Physics B - Proceedings Supplements. 209(1). 109–114. 2 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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