Calorimeter surrogates

The Kaggle calorimeter challenge uses generative AI to produce a surrogate for the Monte Carlo calculation of a calorimeter response to an incident particle (ATLAS data at LHC calculated with GEANT4).

Overview

The Kaggle calorimeter challenge 1 2 uses generative AI to produce a surrogate for the Monte Carlo calculation of a calorimeter response to an incident particle (ATLAS data at LHC calculated with GEANT4). Variational Auto Encoders, GANs, Normalizing Flows, and Diffusion Models. We also have a surrogate using a Quantum Computer (DWAVE) annealer to generate random samples. We have identified four different surrogates that are available openly from Kaggle and later submissions.

References

  1. https://docs.google.com/document/d/19g0Avj9SYbVH7qSxoVUnnFKeGMuBdD9JCHVmBQB466M/edit?usp=sharing ↩︎

  2. https://calochallenge.github.io/homepage/ ↩︎

Last modified January 29, 2024: Update index.md (c4930b3)