Work

Application of Kolmogorov–Arnold Networks in High Energy Physics

Year: 2024

Type: article

Abstract: Kolmogorov–Arnold Networks represent a recent advancement in machine learning, with the potential to outperform traditional perceptron-based neural networks across various domains as well as provide more interpretability with the use of symbolic formulas and pruning. This study explores the application of KANs to specific tasks in high-energy physics. We evaluate the performance of KANs in distinguishing multijet processes in proton–proton collisions and in reconstructing missing transverse momentum in events involving dark matter.

Source: Moscow University Physics Bulletin

DOI: 10.3103/s0027134924702035

Authors: E. E. Abasov, P. V. Volkov, G. A. Vorotnikov, L. V. Dudko, A. D. Zaborenko, E. S. Iudin, A. A. Markina, M. A. Perfilov

Institution: Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4408738954

FWCI: 0.144

Cites: 19

Cited by: 2

Related to: 10