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ATLAS Note
Report number	ATL-PHYS-PROC-2025-109 ; arXiv:2603.12306
Title	Classifying hadronic objects in ATLAS with ML/AI algorithms
Author(s)	Toffolin, Leonardo (Universita degli Studi di Udine (IT))
Corporate Author(s)	The ATLAS collaboration
Collaboration	ATLAS Collaboration
Publication	2025
Imprint	18 Nov 2025
Number of pages	5
In:	The 32nd International Symposium on Lepton Photon Interactions at High Energies (Lepton Photon 2025), Madison, Wisconsin, Us, 25 - 29 Aug 2025
Subject category	Particle Physics - Experiment
Accelerator/Facility, Experiment	CERN LHC ; ATLAS
Free keywords	Object identification ; Jets ; Machine Learning ; Transformers ; JETETMISS
Abstract	The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed to classify jets according to their origin, distinguishing between quark- and gluon-initiated jets, and identifying hadronically decaying heavy objects such as W bosons and top quarks. This contribution summarises recent developments in constituent-based tagging architectures, including graph neural networks (GNNs) and transformer-based approaches, their performance in simulated and real data, and future perspectives towards data-driven optimisation and model-independent tagging strategies.
Copyright/License	CC-BY-4.0

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