Performance of a geometric deep learning pipeline for HL-LHC particle tracking
Performance of a geometric deep learning pipeline for HL-LHC particle tracking
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DOI：
10.1140/epjc/s10052-021-09675-8
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发表时间：
2021-03
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通讯作者：
X. Ju;D. Murnane;P. Calafiura;Nicholas Choma;S. Conlon;S. Farrell;Yaoyuan Xu;M. Spiropulu;J. Vlimant;A. Aurisano;J. Hewes;G. Cerati;L. Gray;T. Klijnsma;J. Kowalkowski;M. Atkinson;M. Neubauer;G. Dezoort;S. Thais;Aditi Chauhan;A. Schuy;Shih-Chieh Hsu;A. Ballow;A. Lazar
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文献类型：
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作者：
X. Ju;D. Murnane;P. Calafiura;Nicholas Choma;S. Conlon;S. Farrell;Yaoyuan Xu;M. Spiropulu;J. Vlimant;A. Aurisano;J. Hewes;G. Cerati;L. Gray;T. Klijnsma;J. Kowalkowski;M. Atkinson;M. Neubauer;G. Dezoort;S. Thais;Aditi Chauhan;A. Schuy;Shih-Chieh Hsu;A. Ballow;A. Lazar
Exa.TrkX项目已经将几何学习概念（例如度量学习和图形神经网络）应用于HEP粒子跟踪。TrkX的跟踪管道将检测器测量分组以形成跟踪候选并过滤它们。该管道最初使用TrackML数据集（LHC启发的跟踪探测器的模拟）开发，已在其他探测器上进行了演示，包括DUNE Liquid Argon TPC和CMS High-Granularity Calorimeter。本文记录了在完整TrackML数据集上研究Exa.TrkX管道的物理和计算性能所需的新进展，这是使用ATLAS和CMS数据验证管道的第一步。该管道实现了与生产跟踪算法类似的跟踪效率和纯度。对于未来的HEP应用至关重要的是，管道从GPU加速中受益匪浅，其计算需求与事件中的粒子数量接近线性。
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.