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Learning the Geometry of Collider Events with Metric-Aware Deep Sets — arXiv:2609.12024

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12024
License (as archived): CC0-1.0
Archived: 2026-10-16T03:16:01.095Z
SHA-256 of the archived text: 98f35c8073772c792caf1a1b0b03ea90e03d730abc655ac858036b6b09baa25b
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How to cite

Learning the Geometry of Collider Events with Metric-Aware Deep Sets — arXiv:2609.12024 — arXiv (en), https://arxiv.org/abs/2609.12024 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dfd10390700a (Metraipolis archive dc_dfd10390700a, sha256 98f35c8073772c792caf1a1b0b03ea90e03d730abc655ac858036b6b09baa25b)

BibTeX

@misc{dcdfd10390700a,
  title = {Learning the Geometry of Collider Events with Metric-Aware Deep Sets — arXiv:2609.12024},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12024},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dfd10390700a; sha256 98f35c8073772c792caf1a1b0b03ea90e03d730abc655ac858036b6b09baa25b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12024. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 approximate methods surveyed. The architectural constraints are found to improve properties that are not explicitly enforced: across $10^6$ held-out event triplets, triangle-inequality violations fall from 199 for a matched unconstrained network to 2, and the maximum from 149.5 to 5.8 GeV. These results demonstrate that targeted inductive biases can yield fast neural surrogates with substantially improved geometric fidelity.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.LG).
Categorie arXiv: High Energy Physics - Phenomenology (hep-ph) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.12024 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T03:05:44.097Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).