Direct Vertex Reconstruction of Λ Baryons from Hits in CLAS12 using Graph Neural Networks

2025/07/02 by Keegan Menkce, Menkce, Keegan, Matthew McEneaney +3
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions

paper · pdf · doi:10.48550/arxiv.2507.01868

openalex publication_date 2025/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Machine learning techniques, including Graph Neural Networks (GNNs), have been used extensively for data analysis in high energy and nuclear physics. Here we report on the use of a GNN to reconstruct decay vertices of Λ hyperons directly from hits in the tracking detector at the CLAS12 experiment at Jefferson Laboratory (JLab). We show that we can improve the vertex reconstruction in simulation compared to the standard, track based, algorithm. We believe this warrants further study. The current study is limited by available training resources but points to an interesting possibility to forgo vertex reconstruction by track fitting in a complicated magnetic field for a more direct approach where the hit to vertex mapping is encoded in a neural network.

Citations

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Relational inductive biases, deep learning, and graph networks
• Review of Particle Physics

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