- Title Information
- Title
- Learning the Geometry of Collider Events with Metric-Aware Deep Sets
- Type of Resource (primo)
- manuscripts
- Type of Resource (primo)
- manuscripts
- Abstract
- 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.
- Name
- Name Part
- Lauren Hay
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Name
- Name Part
- Rishabh Jain
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Name
- Name Part
- Matt LeBlanc
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Name
- Name Part
- Jennifer Roloff
- Role
- Role Term (marcrelator)
(authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
- Author
- Origin Information
- Date Created
- 2026-09-09
- Subject (Local)
- Topic
- Optimal Transport
- Subject (Local)
- Topic
- Large Hadron Collider
- Subject (Local)
- Topic
- Particle Physics
- Subject (Local)
- Topic
- AI/ML
- Subject (Local)
- Topic
- metric spaces
- Subject (Local)
- Topic
- deep sets
- Subject (Local)
- Topic
- neural surrogates
- Access Condition:
use and reproduction
(href="https://creativecommons.org/licenses/by/4.0/legalcode")
- Attribution 4.0 International (CC BY 4.0)
- Access Condition:
logo
(href="https://licensebuttons.net/l/by/4.0/88x31.png")
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Identifier:
DOI
- 10.26300/1f5r-bs32