K. Matchev

86.2k total citations · 2 hit papers
104 papers, 5.2k citations indexed

About

K. Matchev is a scholar working on Nuclear and High Energy Physics, Astronomy and Astrophysics and Artificial Intelligence. According to data from OpenAlex, K. Matchev has authored 104 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Nuclear and High Energy Physics, 26 papers in Astronomy and Astrophysics and 15 papers in Artificial Intelligence. Recurrent topics in K. Matchev's work include Particle physics theoretical and experimental studies (85 papers), Dark Matter and Cosmic Phenomena (36 papers) and Cosmology and Gravitation Theories (25 papers). K. Matchev is often cited by papers focused on Particle physics theoretical and experimental studies (85 papers), Dark Matter and Cosmic Phenomena (36 papers) and Cosmology and Gravitation Theories (25 papers). K. Matchev collaborates with scholars based in United States, Switzerland and South Korea. K. Matchev's co-authors include Jonathan L. Feng, Hsin-Chia Cheng, Damien M. Pierce, Takeo Moroi, Jonathan Bagger, Kyoungchul Kong, Renjie Zhang, Martin Schmaltz, Myeonghun Park and Bogdan A. Dobrescu and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Reviews of Modern Physics.

In The Last Decade

K. Matchev

100 papers receiving 5.1k citations

Hit Papers

Precision corrections in the minimal supersymmetric stand... 1997 2026 2006 2016 1997 2000 200 400 600
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. Precision corrections in the minimal supersymmetric standard model
    1997 629 citations K. Matchev et al. profile →
  2. Neutralino dark matter in focus point supersymmetry
    2000 239 citations K. Matchev et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
K. Matchev United States 36 5.1k 2.5k 186 121 114 104 5.2k
Lian-Tao Wang United States 37 3.9k 0.8× 2.0k 0.8× 134 0.7× 67 0.6× 143 1.3× 110 4.1k
Mikołaj Misiak Poland 31 6.1k 1.2× 944 0.4× 186 1.0× 75 0.6× 115 1.0× 59 6.2k
Matthias Steinhauser Germany 50 7.8k 1.5× 963 0.4× 215 1.2× 118 1.0× 188 1.6× 197 8.0k
F. Boudjema France 34 5.3k 1.0× 3.1k 1.2× 164 0.9× 64 0.5× 169 1.5× 109 5.4k
Johan Alwall United States 16 5.0k 1.0× 1.0k 0.4× 296 1.6× 47 0.4× 76 0.7× 33 5.1k
Michael Trott United States 30 3.5k 0.7× 1.1k 0.5× 74 0.4× 94 0.8× 70 0.6× 62 3.7k
A. Djouadi France 55 11.0k 2.2× 3.8k 1.5× 297 1.6× 88 0.7× 131 1.1× 172 11.1k
Olivier Mattelaer Belgium 20 5.5k 1.1× 1.1k 0.5× 369 2.0× 52 0.4× 83 0.7× 47 5.6k
Valentin Hirschi Switzerland 20 4.7k 0.9× 878 0.4× 284 1.5× 63 0.5× 76 0.7× 31 4.8k
Benjamin Fuks France 30 4.9k 1.0× 1.3k 0.5× 262 1.4× 78 0.6× 62 0.5× 119 5.2k

Countries citing papers authored by K. Matchev

Since Specialization
Citations

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

Fields of papers citing papers by K. Matchev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of K. Matchev

This figure shows the co-authorship network connecting the top 25 collaborators of K. Matchev. A scholar is included among the top collaborators of K. Matchev 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 K. Matchev. K. Matchev 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.
Gleyzer, Sergei, et al.. (2024). A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks. Axioms. 13(3). 160–160. 4 indexed citations
2.
Gleyzer, Sergei, et al.. (2024). Quantum Vision Transformers for Quark–Gluon Classification. Axioms. 13(5). 323–323. 2 indexed citations
3.
Kong, Kyoungchul, et al.. (2024). MTN is all you need: Production of multiple semi-invisible resonances at hadron colliders. Modern Physics Letters A. 39(31n32). 1 indexed citations
4.
Gleyzer, Sergei, et al.. (2024). ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks. Axioms. 13(3). 188–188. 2 indexed citations
5.
Smolinsky, Jordan, et al.. (2023). Signatures and detection prospects for sub-GeV dark matter with superfluid helium. Journal of High Energy Physics. 2023(7). 4 indexed citations
6.
Matchev, K., et al.. (2023). A Quantum Algorithm for Model-Independent Searches for New Physics. SHILAP Revista de lepidopterología. 2023. 2 indexed citations
7.
Matchev, K., et al.. (2023). Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection. The Astrophysical Journal. 958(2). 106–106. 1 indexed citations
8.
Matchev, K., et al.. (2023). Oracle-Preserving Latent Flows. Symmetry. 15(7). 1352–1352. 6 indexed citations
9.
Matchev, K., et al.. (2023). Deep learning symmetries and their Lie groups, algebras, and subalgebras from first principles. Machine Learning Science and Technology. 4(2). 25027–25027. 10 indexed citations
10.
Kong, Kyoungchul, et al.. (2023). Is the machine smarter than the theorist: Deriving formulas for particle kinematics with symbolic regression. Physical review. D. 107(5). 11 indexed citations
11.
Franceschini, Roberto, et al.. (2023). Kinematic variables and feature engineering for particle phenomenology. Reviews of Modern Physics. 95(4). 10 indexed citations
12.
Kim, Doojin, et al.. (2023). Deep-learned event variables for collider phenomenology. Physical review. D. 107(3). 6 indexed citations
13.
Matchev, K., et al.. (2022). Analytical Modeling of Exoplanet Transit Spectroscopy with Dimensional Analysis and Symbolic Regression. The Astrophysical Journal. 930(1). 33–33. 28 indexed citations
14.
Matchev, K., et al.. (2022). Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra. The Planetary Science Journal. 3(9). 205–205. 12 indexed citations
15.
Matchev, K., et al.. (2022). Superfluid effective field theory for dark matter direct detection. Journal of High Energy Physics. 2022(5). 6 indexed citations
16.
Matchev, K.. (2022). New limits on the SUSY Higgs boson mass. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).
17.
Matchev, K., et al.. (2020). Uncertainties associated with GAN-generated datasets in high energy physics. SHILAP Revista de lepidopterología. 16 indexed citations
18.
Avery, P., D. Bourilkov, Mingshui Chen, et al.. (2012). Precision Studies of the Higgs Golden Channel H -> ZZ* -> 4l. Part I. Kinematic discriminants from leading order matrix elements. arXiv (Cornell University). 4 indexed citations
19.
Cho, Won‐Sang, Doojin Kim, K. Matchev, & Myeonghun Park. (2012). Cracking the dark matter code at the LHC. arXiv (Cornell University). 7 indexed citations
20.
Barr, A. J., T. J. Khoo, Partha Konar, et al.. (2011). A storm in a \T" cup: the connoisseur's guide to transverse projections and mass-constraining variables. arXiv (Cornell University). 1 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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