A. Tykhonov

59.1k total citations
20 papers, 61 citations indexed

About

A. Tykhonov is a scholar working on Nuclear and High Energy Physics, Radiation and Aerospace Engineering. According to data from OpenAlex, A. Tykhonov has authored 20 papers receiving a total of 61 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Nuclear and High Energy Physics, 5 papers in Radiation and 3 papers in Aerospace Engineering. Recurrent topics in A. Tykhonov's work include Particle Detector Development and Performance (13 papers), Dark Matter and Cosmic Phenomena (10 papers) and Astrophysics and Cosmic Phenomena (6 papers). A. Tykhonov is often cited by papers focused on Particle Detector Development and Performance (13 papers), Dark Matter and Cosmic Phenomena (10 papers) and Astrophysics and Cosmic Phenomena (6 papers). A. Tykhonov collaborates with scholars based in Switzerland, China and Italy. A. Tykhonov's co-authors include X. Wu, David Droz, G. Ambrosi, D. Y. Guo, V. Gallo, Chuan Yue, Ke Gong, S. Jézéquel, Chi Wang and R. Asfandiyarov and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment and Radiation Physics and Chemistry.

In The Last Decade

A. Tykhonov

16 papers receiving 55 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A. Tykhonov Switzerland 5 49 12 8 7 6 20 61
K. Herner United States 4 19 0.4× 10 0.8× 6 0.8× 7 1.0× 20 35
S. Schlenker Switzerland 3 30 0.6× 15 1.3× 9 1.1× 9 1.3× 8 42
R. Zhang United States 5 62 1.3× 4 0.3× 7 0.9× 4 0.6× 9 67
A. Ruiz-Martínez Spain 5 67 1.4× 18 1.5× 22 2.8× 3 0.4× 3 0.5× 17 77
J. Boudreau United States 5 39 0.8× 14 1.2× 10 1.3× 1 0.1× 3 0.5× 17 42
S. Miglioranzi United Kingdom 3 204 4.2× 9 0.8× 12 1.5× 2 0.3× 4 0.7× 3 211
P. J. Konopka Switzerland 5 13 0.3× 15 1.3× 9 1.1× 4 0.6× 10 34
S. Easo Switzerland 2 116 2.4× 6 0.5× 16 2.0× 1 0.1× 5 0.8× 2 124
Vitali Choutko United States 4 39 0.8× 5 0.4× 2 0.3× 11 1.6× 3 0.5× 13 41
A. Di Mattia Italy 4 26 0.5× 8 0.7× 5 0.6× 7 1.0× 11 31

Countries citing papers authored by A. Tykhonov

Since Specialization
Citations

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

Fields of papers citing papers by A. Tykhonov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. Tykhonov

This figure shows the co-authorship network connecting the top 25 collaborators of A. Tykhonov. A scholar is included among the top collaborators of A. Tykhonov 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 A. Tykhonov. A. Tykhonov 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.
Tarasov, V. A., et al.. (2023). Geant4 simulation of the moderating neutrons spectrum. Radiation Physics and Chemistry. 212. 111151–111151. 2 indexed citations
2.
Droz, David, A. Tykhonov, X. Wu, & M. Deliyergiyev. (2022). Neural networks for TeV cosmic electrons identification on the DAMPE experiment. 45–45. 1 indexed citations
3.
Stolpovskiy, M., et al.. (2022). Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment. Journal of Instrumentation. 17(6). P06031–P06031.
4.
Tykhonov, A., Paul Coppin, M. Deliyergiyev, et al.. (2022). A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission. Astroparticle Physics. 146. 102795–102795. 5 indexed citations
5.
Droz, David, et al.. (2021). Machine learning methods for helium flux analysis with DAMPE experiment. Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021). 77–77.
6.
Droz, David, A. Tykhonov, X. Wu, et al.. (2021). A neural network classifier for electron identification on the DAMPE experiment. Journal of Instrumentation. 16(7). P07036–P07036. 8 indexed citations
7.
Rico, J., N. Mōri, F. Gargano, et al.. (2021). Gamma-ray performance study of the HERD payload. Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021). 651–651. 6 indexed citations
8.
Droz, David, A. Tykhonov, & X. Wu. (2019). Neural Networks for Electron Identification with DAMPE. Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019). 64–64. 3 indexed citations
9.
Tykhonov, A., et al.. (2019). TeV--PeV hadronic simulations with DAMPE. Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019). 143–143. 4 indexed citations
10.
Ambrosi, G., P. Azzarello, B. Bergmann, et al.. (2019). The Penetrating particle ANalyzer (PAN) instrument for measurements of low energy cosmic rays. CERN Document Server (European Organization for Nuclear Research). 1–8.
11.
Qiao, Rui, Wen-Xi Peng, D. Y. Guo, et al.. (2018). Charge reconstruction of the DAMPE Silicon–Tungsten Tracker: A preliminary study with ion beams. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 886. 48–52. 9 indexed citations
12.
Tykhonov, A., V. Gallo, X. Wu, & S. Zimmer. (2017). Reconstruction software of the silicon tracker of DAMPE mission. Journal of Physics Conference Series. 898. 42031–42031. 1 indexed citations
13.
Wang, Chi, Yifeng Wei, Zhiyong Zhang, et al.. (2017). Offline software for the DAMPE experiment. Chinese Physics C. 41(10). 106201–106201. 4 indexed citations
14.
Caragiulo, M., Jin Chang, Yi-Zhong Fan, et al.. (2016). DAMPE detection of variable GeV gamma-ray emission from blazar CTA 102. ATel. 9901. 1. 1 indexed citations
15.
Deliyergiyev, M., et al.. (2016). Internal states of hadrons in relativistic reference frame. Ukrainian Journal of Physics. 61(12). 1033–1047. 1 indexed citations
16.
Tykhonov, A., Chi Wang, & X. Wu. (2016). Software framework and reconstruction software of the DAMPE gamma-ray telescope. Proceedings of The 34th International Cosmic Ray Conference — PoS(ICRC2015). 1193–1193. 2 indexed citations
17.
Tykhonov, A.. (2016). Offline and CAD-GEANT4 software of the DAMPE mission. 104–104. 1 indexed citations
18.
Tykhonov, A., et al.. (2013). On equivalence of gluon-loop exchange in the inelastic processes in perturbative QCD to pion exchange inɸ3theory. SHILAP Revista de lepidopterología. 60. 20018–20018. 1 indexed citations
19.
Stewart, G. A., V. Garonne, M. Lassnig, et al.. (2012). Advances in service and operations for ATLAS data management. Journal of Physics Conference Series. 368. 12005–12005. 2 indexed citations
20.
Tykhonov, A., M. Lassnig, V. Garonne, et al.. (2011). Popularity framework to process dataset traces and its application on dynamic replica reduction in the ATLAS experiment. Journal of Physics Conference Series. 331(6). 62018–62018. 10 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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