Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However...
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| creator | Fox, Patrick J Huang, Shangqing Isaacson, Joshua Ju, Xiangyang Nachman, Benjamin |
| description | Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge. |
| doi_str_mv | 10.48550/arxiv.2012.04533 |
| format | Article |
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| subjects | Algorithms Clusters Combinatorial analysis Large Hadron Collider Luminosity Neural networks Parameter estimation Pattern recognition Physics - Data Analysis, Statistics and Probability Physics - High Energy Physics - Experiment Physics - High Energy Physics - Phenomenology Physics - Instrumentation and Detectors Statistics - Machine Learning Tracking |
| title | Beyond 4D Tracking: Using Cluster Shapes for Track Seeding |
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