19–23 May 2025
CERN
Europe/Zurich timezone

Scalable Multi-Task Learning for Event Reconstruction with Heterogeneous Graph Neural Networks

23 May 2025, 11:10
20m
222/R-001 (CERN)

222/R-001

CERN

200
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Contributed talk 1 ML for object identification and reconstruction Contributed Talks

Speaker

William Sutcliffe (University of Zurich (CH))

Description

The growing luminosity frontier at the Large Hadron Collider is complicating the reconstruction of heavy-hadron collision events both at data acquisition and offline levels with rising particle multiplicities challenging stringent latency and storage requirements. This talk presents significant architectural advancements in Graph Neural Networks (GNNs) aimed at enhancing event reconstruction in high-energy physics. These advancements are implemented and evaluated within the context of expanding the deep full event interpretation (DFEI) framework [García Pardiñas, J., et al. Comput. Softw. Big Sci. 7 (2023) 1, 12], which targets the hierarchical reconstruction of B-hadron decays within the hadronic collision environment of the LHCb experiment.

Specifically, we introduce a novel end-to-end Heterogeneous Graph Neural Network (HGNN) architecture, which allows for unique representations for several particle collision relations and features integrated edge and node pruning layers. The HGNN is trained using a multi-task paradigm, which not only significantly enhances the B-hadron reconstruction performance but also simultaneously enables primary vertex association and graph pruning tasks within a single, unified model. We will discuss the performance improvements achieved, quantifying both the reconstruction accuracy and the effectiveness of the pruning. Furthermore, we propose a weighted message passing scheme designed to improve the model's inference time scalability with minimal performance loss, a key consideration for deployment in high-throughput environments.

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Author

William Sutcliffe (University of Zurich (CH))

Co-authors

Abhijit Mathad (CERN) Azusa Uzuki (University of Zurich (CH)) Jonas Eschle (Syracuse University (US)) Julian Garcia Pardinas (Massachusetts Inst. of Technology (US)) Marta Calvi (Univ. degli Studi Milano-Bicocca) Nicola Serra (University of Zurich (CH)) Simone Capelli (Universita & INFN, Milano-Bicocca (IT))

Presentation materials