Speaker
Description
Modern high energy physics experiments fundamentally rely on accurate simulation- both to characterise detectors and to connect observed signals to underlying theory. Traditional simulation tools are reliant upon Monte Carlo methods which, while powerful, consume significant computational resources. These computing pressures are projected to become a major bottleneck at the high luminosity stage of the LHC and for future colliders. Deep generative models hold promise to potentially offer significant reductions in compute times, while maintaining a high degree of physical fidelity.
This contribution provides an overview of a growing body of work focused on simulating showers in highly granular calorimeters, which is making significant strides towards realising fast simulation tools based on deep generative models. Progress on the simulation of both electromagnetic and hadronic showers, as well further steps to address challenges faced when broadening the scope of these simulators, will be reported. A particular focus will be placed on the high degree of physical fidelity achieved, as well as the performance after interfacing with reconstruction algorithms.
| Consider for long presentation | Yes |
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