13 papers
Virtues and Vices of Equivariant Transformers
Luigi Favaro, Tilman Plehn, Huilin Qu +1
We study for the first time the benefit of Lorentz-equivariant transformers for large-size jet tagging and flavor tagging. To control their computing demands, we optimize all imple…
A universal vision transformer for fast calorimeter simulations
Luigi Favaro, Andrea Giammanco, Claudius Krause
The high-dimensional complex nature of detectors makes fast calorimeter simulations a prime application for modern generative machine learning. Vision transformers (ViTs) can emula…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
Fast, accurate, and precise detector simulation with vision transformers
Luigi Favaro, Andrea Giammanco, Claudius Krause
The speed and fidelity of detector simulations in particle physics pose compelling questions about LHC analysis and future colliders. The sparse high-dimensional data, combined wit…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
Lorentz-Equivariance without Limitations
Luigi Favaro, Gerrit Gerhartz, Fred A. Hamprecht +5
Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts…