Details

Autor(en) / Beteiligte

• Caillou, Sylvain
• Calafiura, Paolo
• Ju, Xiangyang
• Murnane, Daniel
• Pham, Tuan
• Rougier, Charline
• Stark, Jan
• Vallier, Alexis
• Espinal, X.
• Shadura, O.
• De Vita, R.
• Laycock, P.

Titel

Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain

Ist Teil von

• EPJ Web of Conferences, 2024, Vol.295, p.3030

Ort / Verlag

Les Ulis: EDP Sciences

Erscheinungsjahr

2024

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Particle tracking is vital for the ATLAS physics programs. To cope with the increased number of particles in the High Luminosity LHC, ATLAS is building a new all-silicon Inner Tracker (ITk), consisting of a Pixel and a Strip subdetector. At the same time, ATLAS is developing new track reconstruction algorithms that can operate in the HL-LHC dense environment. A track reconstruction algorithm needs to solve two problems: track finding for building track candidates and track fitting for obtaining track parameters of those track candidates. Previously, we developed GNN4ITk, a track-finding algorithm based on a Graph Neural Network (GNN), and achieved good track-finding performance under realistic HL-LHC conditions. Our GNN pipeline relied only on the 3D spacepoint positions. This work introduces heterogeneous GNN models to fully exploit the subdetector-dependent features of ITk data, improving the performance of our GNN4ITk pipeline. In addition, we interfaced our pipeline to the standard ATLAS track-fitting algorithm and data model. With that, the GNN4ITk pipeline produces full-fledged track candidates that can be used for any downstream analyses and compared with the other track reconstruction algorithms.