Simulation of calorimeter response is important for modern high energy physics experiments. With 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 two-stage generative model which is similar to recently...
The efficient simulation of particle propagation and interaction within the detectors of the Large Hadron Collider (LHC) 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. ...
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint.
This work achieves a major breakthrough in this task by for the first time directly...
We apply CaloFlow to GEANT4 showers of Dataset 1, producing high-fidelity samples with a sampling time of less than 0.1ms per shower. We validated the fidelity of the samples using multiple metrics, including a classifier metric. To generalize CaloFlow to the higher dimensional Datasets 2 and 3, we propose a new approach called Inductive CaloFlow. This approach involves training the flow on...
Normalizing flows are a type of generative models that can be trained directly by minimizing the negative log-likelihood. It has been shown that flows can accurately model showers in low complexity calorimeters. We show how normalizing flows can be improved and adapted to accurately model showers in calorimeters with significantly higher complexity. One of the key points here is to move away...
In particle physics, precise simulations of the interaction processes in calorimeters are essential for scientific discovery. However, accurate simulations using GEANT4 are computationally very expensive and pose a major challenge for the future of particle physics. In this study, we apply the CaloPointFlow model, a novel generative model based on normalizing flows, to fast and high-fidelity...
The simulation of electromagnetic and hadronic interactions in calorimeters is a very demanding process, both in terms of time and computing resources. A novel technique based on Generative Adversarial Networks (GANs) may benefit from a more efficient use of computing resources, although initial training could be computationally demanding. Nowadays and in the near future we expect to have more...
We propose the application of our previously published Cross AtteNtion meAn-fielD mAtching CANADA-GAN for generating particle showers in high-granularity datasets. Results are presented for dataset 2 and 3. Point cloud generative models are known to benefit from higher granularity, making these datasets well-suited for high-granularity calorimeters. Although the regular architecture of the...
