Abstract
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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Acknowledgments
We thank David Shih and Edward Ramirez for the useful comments and discussions. This work is supported by Grant-in-Aid for Transformative Research Area (A) 22H05113 and Grant-in-Aid for Scientific Research(C) JSPS KAKENHI Grant Number 22K03629. The work of SHL was also supported by the US Department of Energy under grant DE-SC0010008. The authors acknowledge the Office of Advanced Research Computing (OARC) at Rutgers, The State University of New Jersey for providing access to the Amarel cluster and associated research computing resources that have contributed to the results reported here (URL: https://oarc.rutgers.edu). This paper is revised using large language models, ChatGPT4 and Claude 3 Opus. The authors guarantee that the AI tools are used only to improve writing quality, not to generate the idea and paper itself.
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ArXiv ePrint: 2312.11760
Amon Furuichi is on leave to Sokendai.
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Furuichi, A., Lim, S.H. & Nojiri, M.M. Jet classification using high-level features from anatomy of top jets. J. High Energ. Phys. 2024, 146 (2024). https://doi.org/10.1007/JHEP07(2024)146
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DOI: https://doi.org/10.1007/JHEP07(2024)146