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 consider
the top jet tagging problems and 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) while requiring fewer computational resources
in a comparison of top jet tagging using jets simulated at the hadronic
calorimeter angular resolution scale. 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. Lastly, we
briefly comment on the results of AM with finer angular resolution inputs.
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