2021/04/30 by Giorgio Cerro, G. Cerro, Srinandan Dasmahapatra +8
Mathematics · Physics and Astronomy · #Artificial intelligence #Bar (unit) #Cluster analysis #Combinatorics #Computer science #High-Energy Particle Collisions Research #Jet (fluid) #Mathematics #Nuclear physics #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Physics #Thermodynamics #hep-ex #hep-ph
paper · pdf · doi:10.1007/jhep02(2022)165
31 pages, 17 figures
arxiv created 2022/01/28 · openalex publication_date 2022/02/01 · arxiv updated 2022/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present a new approach to jet definition alternative to clustering methods, such as the anti-kT scheme, that exploit kinematic data directly. Instead the new method uses kinematic information to represent the particles in a multidimensional space, as in spectral clustering. After confirming its Infra-Red (IR) safety, we compare its performance in analysing qq→ H125 GeV → H40 GeV H40 GeV → b b b b, qq→ H500 GeV → H125 GeV H125 GeV → b b b b and gg,q q→ t t→ b b W+W-→ b b jj ℓν_ℓ events from Monte Carlo (MC) samples, specifically, in reconstructing the relevant final states, to that of the anti-kT algorithm. Finally, we show that the results for spectral clustering are obtained without any change in the parameter settings of the algorithm, unlike the anti-kT case, which requires the cone size to be adjusted to the physics process under study.