Learning the Geometry of Collider Events with Metric-Aware Deep Sets
Optimal transport gives structured data a geometry, but exact evaluation is costly in large pairwise analyses that exploit relationships among distances. Learned surrogates are faster, but need not preserve this metric structure. We develop a Deep Sets surrogate for OT between variable-size weighted point clouds that enforces non-negativity, exchange symmetry, and zero self-distance, leaving the triangle inequality unconstrained. Applied to the Energy Mover's Distance between collider events in a particle physics application, the Metric-Aware Particle Flow Network achieves percent-level mean absolute percentage error while significantly improving inference throughput over other exact and ...
| Authors | Lauren Hay, Rishabh Jain, Matt LeBlanc et al. |
|---|---|
| Primary category | hep-ph |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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