Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion

arXiv:2608.27077 · hep-ph, cs.AI · Submitted 2026-08-27 · Read on arXiv

hep-ph, cs.AI

Submitted: 2026-08-27

Updated: 2026-08-27

Comments: Accepted at Physics and AI at Stanford University (PAI 2026)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 11× in this paper · explained in 8 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Uncertainty Quantification — 3× in this paper · explained in 8 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes

Sources

• Toward an event-level analysis of hadron structure using differential programming
• Adam: A Method for Stochastic Optimization

Related papers

• Classification of g-modes for neutron stars with a strong transition: Novel universal relation including slow stable hybrid stars
• Higgsino Dark Matter Interpretation of the LUX-ZEPLIN 248 keV Nuclear-Recoil Event
• A Unified Bogoliubov Approach to Primordial Gravitational Waves: From Inflation to Reheating
• Probing Memory-Burdened Primordial Black Holes with High-Energy Neutrinos
• Enhanced Dark Matter Quantum Sensing via Phase-Space Geometric Interferometry
• Axions as Dark Matter, Dark Energy, and Dark Radiation