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High Pileup Particle Tracking with Object Condensation
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Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the HL-LHC. Most GNN tracking algorithms are based on edge classification and identify tracks as connected components from an initial graph containing spurious connections. In this talk, we consider an alternative based on object condensation (OC), a multi-objective learning framework designed to cluster points (hits) belonging to an arbitrary number of objects (tracks) and regress the properties of each object. Building on our previous results, we present a streamlined model and show progress toward a one-shot OC tracking algorithm in a high-pileup environment.
Forward citations
Cited by 2 Pith papers
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Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II
A Transformer-based hit classifier improves MEG II positron tracking efficiency and resolution, yielding an expected ~10% gain in μ→eγ sensitivity.
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Physics and Computing Performance of the EggNet Tracking Pipeline
EggNet on the full TrackML dataset achieves around 96% track efficiency, and segmented graph training cuts training time and GPU memory by roughly 10x with comparable physics performance.
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