Regular Article - Theoretical Physics
Exploring the universality of hadronic jet classification
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
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
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.