Year: 2024
Type: conference-paper
Abstract: The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration, employing innovative approaches including advanced neural network architectures, attention-based mechanisms, and adversarial training. These proceedings provide a comprehensive review of the state-of-the-art methods employed by both collaborations, highlighting their similarities, unique strengths, and limitations through a comparative analysis.
DOI: 10.22323/1.478.0150
Author: Andrea Malara
Institution: Université Libre de Bruxelles
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405885204