14 citations · 26 across the 6 of their papers we have counts for
6 papers
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…
Via Machinae 3.0: A search for stellar streams in Gaia with the CATHODE algorithm
Anna Hallin, David Shih, Claudius Krause +1
We apply the model-agnostic anomaly detection method Cathode - originally developed for particle physics - to search for stellar streams in Gaia data. We combine Cathode with Via M…
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…
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
Thorsten Buss, Frank Gaede, Gregor Kasieczka +2
In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial fronti…
Unifying Simulation and Inference with Normalizing Flows
Haoxing Du, Claudius Krause, Vinicius Mikuni +3
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector si…