End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

Understand

High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics.

• However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors.
• Methods to solve this \textit{inverse problem} of mapping detector observations to theoretical quantities of the underlying collision are essential parts of many physics analyses at the LHC.
• We investigate and compare various generative deep learning methods to approximate this inverse mapping.