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arXiv Machine Learning By Callum Birch-Sykes, Brian Le, Yvonne Peters, Ethan Simpson, Zihan Zhang

Reconstructing short-lived particles using hypergraph representation learning

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The paper introduces HyPER, a hypergraph-based graph neural network architecture designed to reconstruct short-lived particles in collider experiments. By leveraging hypergraph representation learning, HyPER builds more powerful and efficient representations of collider events, enabling accurate reconstruction of parent particles from final-state objects. In simulations, HyPER outperforms existing state‑of‑the‑art techniques while using fewer parameters, and its flexible hypergraph approach can be applied to a wide range of physics processes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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