Heterogeneous Graph Neural Network for Identifying Hadronically Decayed Tau Leptons at the High Luminosity LHC

Autor:	Huang, Andris, Ju, Xiangyang, Lyons, Jacob, Murnane, Daniel, Pettee, Mariel, Reed, Landon
Rok vydání:	2023
Předmět:	Physics - Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology
Druh dokumentu:	Working Paper
DOI:	10.1088/1748-0221/18/07/P07001
Popis:	We present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous graphs with tracks and energy clusters as nodes and trains a Graph Neural Network to identify tau jets from other jets. Different attributed graph representations and different GNN architectures are explored. We propose to use differential track and energy cluster information as node features and a heterogeneous sequentially-biased encoding for the inputs to final graph-level classification. Comment: 14 pages, 10 figures, 4 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.00501 Zobrazit plný text záznamu View this record from Arxiv