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Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain.
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- المؤلفون: Caillou, Sylvain; Calafiura, Paolo; Ju, Xiangyang; Murnane, Daniel; Pham, Tuan; Rougier, Charline; Stark, Jan; Vallier, Alexis
- المصدر:
EPJ Web of Conferences; 5/6/2024, Vol. 295, p1-7, 7p
- الموضوع:
- معلومة اضافية
- نبذة مختصرة :
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. [ABSTRACT FROM AUTHOR]
- نبذة مختصرة :
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