https://doi.org/10.1140/epjs/s11734-024-01256-6
Review
Interplay of traditional methods and machine learning algorithms for tagging boosted objects
1
Center for High Energy Physics, Indian Institute of Science, 560012, Bangalore, India
2
Department of Physics, SRM University AP, 522240, Amaravati, India
3
School of Science & Technology, Vijaybhoomi University, 410201, Greater Mumbai, India
Received:
1
April
2024
Accepted:
16
July
2024
Published online:
31
July
2024
Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural networks is one of the well-established areas. In this review, we discuss different tagging frameworks available to tag boosted objects, especially boosted Higgs boson and top quark, at the Large Hadron Collider (LHC). Our aim is to study the interplay of traditional jet substructure-based methods with the state-of-the-art machine learning ones. In this methodology, we would gain some interpretability of those machine learning methods, and which in turn helps to propose hybrid taggers relevant for tagging of those boosted objects belonging to both Standard Model (SM) and physics beyond the SM.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
