Speaker
Description
Calorimeter simulations based on Monte Carlo methods (Geant4), while accurate, are computationally expensive and time-consuming. In this regard, numerous efforts aim to accelerate these simulations faster via generative machine learning. Although these machine learning models tend to be faster than Geant4, their design demands a significant amount of time, computational resources, and manpower. These factors limit the use of such models for new detector geometries. To mitigate this issue, inspired by foundation models (GPT-3, Dall.E 2, OpenAI Whisper), we investigate the idea of reusing the knowledge acquired by our transformer-based diffusion model when trained on various detector geometries. Our model shows robust generalization to new detector geometries while requiring substantially less training time and data. Furthermore, we present our findings on applying various methods to address the well-known issue of slow sampling speed of diffusion models.
| Track | Detector simulation & event generation |
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