Exploring the Universality of Hadronic Jet Classification
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Abstract (arXiv)
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.
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References · 9
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- 2017Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesiscited by 46
- 2015Jet-Images -- Deep Learning Editioncited by 41
- 2016Deep learning in color: towards automated quark/gluon jet discriminationcited by 21
- 2018INFERNO: Inference-Aware Neural Optimisationcited by 16
- 2022neos: End-to-End-Optimised Summary Statistics for High Energy Physicscited by 10
- 2020Adversarial domain adaptation to reduce sample bias of a high energy physics classifiercited by 9
- 2022Improving Robustness of Jet Tagging Algorithms with Adversarial Trainingcited by 6
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