Conveners
Generative Models -- Detector Level
- Ramon Winterhalder (UC Louvain)
- Kevin Pedro (Fermi National Accelerator Lab. (US))
Generative Models -- Detector Level
- Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))
- Claudius Krause (Rutgers University)
AtlFast3 is the new, high-precision fast simulation in ATLAS that was deployed by the collaboration to replace AtlFastII, the fast simulation tool that was successfully used for most of Run2. AtlFast3 combines a parametrization-based Fast Calorimeter Simulation and a new machine-learning-based Fast Calorimeter Simulation based on Generative Adversarial Networks (GANs). The new fast simulation...
Simulating particle detector response is the single most computationally expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy (CaloFlow).
Applying CaloFlow to the photon and charged pion GEANT4 showers of Dataset 1 of the Fast Calorimeter Simulation...
Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. Three...
The efficient simulation of particle propagation and interaction within the detectors of the Large Hadron Collider is of primary importance for precision measurements and new physics searches. The most computationally expensive simulations involve calorimeter showers, which will become ever more costly and high-dimensional as the Large Hadron Collider moves into its High Luminosity era....
Simulation in High Energy Physics (HEP) places a heavy burden on the available computing resources and is expected to become a major bottleneck for the upcoming high luminosity phase of the LHC and for future Higgs factories, motivating a concerted effort to develop computationally efficient solutions. Methods based on generative machine learning methods hold promise to alleviate the...
Simulation of calorimeter response is important for modern high energy physics experiments. With the increasingly large and high granularity design of calorimeters, the computational cost of conventional MC-based simulation of each particle-material interaction is becoming a major bottleneck. We propose a new generative model based on a Vector-Quantized Variational Autoencoder (VQ-VAE) to...
A realistic detector simulation is an essential component of experimental particle physics. However, it is currently very inefficient computationally since large amounts of resources are required to produce, store, and distribute simulation data. Deep generative models allow for more cost-efficient and faster simulations. Nevertheless, generating detector responses is a highly non-trivial task...
