Speaker
Description
Simulation is a crucial part of all aspects of collider data analysis. However, the computing challenges of the High Luminosity era will require simulation to use a smaller fraction of computing resources, at the same time as more complex detectors are introduced, requiring more detailed simulation. This motivates the use of machine learning (ML) models as surrogates to replace full physics-based detector simulation. Recently in the ML community, a new class of models based on diffusion have become state of the art for generating high quality images with reasonable computation times. In this work, we study the application of diffusion models to generate simulated calorimeter showers. In order to reduce the computational burden of the method, we explore compressing the calorimeter shower into a smaller latent space for the diffusion process. Optimization of this latent space, the handling of irregular detector geometries, and comparisons to other generative models will be discussed. We will also discuss the possibility of using diffusion models to enhance, or denoise, existing physics-based fast simulations as an alternative to the fully generative approach.
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