Generative diffusion and flow models are constructed to remain exactly on the Lorentz-invariant massless N-particle phase space manifold during sampling for particle physics applications.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
method 2
citation-polarity summary
fields
hep-ph 4roles
method 2polarities
use method 2representative citing papers
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
Collider events are represented as multivectors in Cl(1,3) ⊗ V_flav whose grade projections recover standard observables, intended as input for equivariant foundation models.
citing papers explorer
-
Generative models on phase space
Generative diffusion and flow models are constructed to remain exactly on the Lorentz-invariant massless N-particle phase space manifold during sampling for particle physics applications.
-
One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
-
Geometric algebra as the input language of collider foundation models
Collider events are represented as multivectors in Cl(1,3) ⊗ V_flav whose grade projections recover standard observables, intended as input for equivariant foundation models.
- KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging