https://doi.org/10.1140/epjc/s10052-022-11084-4
Regular Article - Theoretical Physics
Exploring the universality of hadronic jet classification
1
Department of Physics and Center for Theory and Computation, National Tsing Hua University, 300, Hsinchu, Taiwan
2
Department of Physics, University of Washington, 98195, Seattle, WA, USA
3
Physics Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
4
Berkeley Institute for Data Science, University of California, 94720, Berkeley, CA, USA
5
Division of Quantum Phases and Devices,School of Physics, Konkuk University, 143-701, Seoul, Republic of Korea
Received:
21
April
2022
Accepted:
29
November
2022
Published online:
22
December
2022
The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.
© The Author(s) 2022
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