Kavli Affiliate: Mihoko M. Nojiri
| First 5 Authors: Amon Furuichi, Sung Hak Lim, Mihoko M. Nojiri, ,
| Summary:
Recent advancements in deep learning models have significantly enhanced jet
classification performance by analyzing low-level features (LLFs). However,
this approach often leads to less interpretable models, emphasizing the need to
understand the decision-making process and to identify the high-level features
(HLFs) crucial for explaining jet classification. To address this, we introduce
an analysis model (AM) that analyzes selected HLFs designed to capture
important features of top jets. Our AM mainly consists of the following three
modules: a relation network analyzing two-point energy correlations,
mathematical morphology and Minkowski functionals for generalizing jet
constituent multiplicities, and a recursive neural network analyzing subjet
constituent multiplicity to enhance sensitivity to subjet color charges. We
demonstrate that our AM achieves performance comparable to the Particle
Transformer (ParT) in top jet tagging and simulated jet comparison at the
hadronic calorimeter angular resolution scale while requiring fewer
computational resources. Furthermore, as a more constrained architecture than
ParT, the AM exhibits smaller training uncertainties because of the
bias-variance tradeoff. We also compare the information content of AM and ParT
by decorrelating the features already learned by AM.
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