Interplay of traditional methods and machine learning algorithms for tagging boosted objects

Bose, C and Chakraborty, A and Chowdhury, S and Dutta, S (2024) Interplay of traditional methods and machine learning algorithms for tagging boosted objects. In: European Physical Journal: Special Topics .

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Abstract

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. © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Item Type:	Journal Article
Publication:	European Physical Journal: Special Topics
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to the publishers.
Department/Centre:	Division of Physical & Mathematical Sciences > Centre for High Energy Physics
Date Deposited:	18 Oct 2024 09:54
Last Modified:	18 Oct 2024 09:54
URI:	http://eprints.iisc.ac.in/id/eprint/86393

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