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Description
We are happy to announce the start of the first-ever Fast Calorimeter Simulation Data Challenge!!
The purpose of this challenge is to spur the development and benchmarking of fast and high-fidelity calorimeter shower generation using deep learning methods. Currently, generating calorimeter showers of interacting particles (electrons, photons, pions, ...) using GEANT4 is a major computational bottleneck at the LHC, and it is forecast to overwhelm the computing budget of the LHC experiments in the near future. Therefore there is an urgent need to develop GEANT4 emulators that are both fast (computationally lightweight) and accurate. The LHC collaborations have been developing fast simulation methods for some time, and the hope of this challenge is to directly compare new deep learning approaches on common benchmarks. It is expected that participants will make use of cutting-edge techniques in generative modeling with deep learning, e.g. GANs, VAEs and normalizing flows.
For more details, including information about the datasets, metrics and timeline, please visit the CaloChallenge homepage here:https://calochallenge.github.io/homepage/
To ask questions or participate in discussions about the challenge, please join the #calochallenge channel on this Slack workspace.
Finally, please consider joining the Google group mailing list https://groups.google.com/g/calochallenge to receive occasional announcements.
Good luck and have fun!!
Sincerely,
The CaloChallenge2022 Organizing Team (Michele Faucci Gianelli, Gregor Kasieczka, Claudius Krause, Ben Nachman, Dalila Salamani, David Shih and Anna Zaborowska)
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