Abstract

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.

Details

Title
Exploring the universality of hadronic jet classification
Author
Cheung, Kingman 1 ; Chung, Yi-Lun 2   VIAFID ORCID Logo  ; Hsu, Shih-Chieh 3 ; Nachman, Benjamin 4 

1  National Tsing Hua University, Department of Physics and Center for Theory and Computation, Hsinchu, Taiwan (GRID:grid.38348.34) (ISNI:0000 0004 0532 0580); Konkuk University, Division of Quantum Phases and Devices,School of Physics, Seoul, Republic of Korea (GRID:grid.258676.8) (ISNI:0000 0004 0532 8339) 
2  National Tsing Hua University, Department of Physics and Center for Theory and Computation, Hsinchu, Taiwan (GRID:grid.38348.34) (ISNI:0000 0004 0532 0580) 
3  University of Washington, Department of Physics, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) 
4  Lawrence Berkeley National Laboratory, Physics Division, Berkeley, USA (GRID:grid.184769.5) (ISNI:0000 0001 2231 4551); University of California, Berkeley Institute for Data Science, Berkeley, USA (GRID:grid.47840.3f) (ISNI:0000 0001 2181 7878) 
Pages
1162
Publication year
2022
Publication date
Dec 2022
Publisher
Springer Nature B.V.
ISSN
14346044
e-ISSN
14346052
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1140/epjc/s10052-022-11084-4
ProQuest document ID
2756862959
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.