Speaker
Description
The detailed simulation of electromagnetic calorimeters (EMC) remains computationally intensive due to simulation of millions of secondary particles.
Machine learning offers a promising alternative by bypassing explicit shower simulation, though its accuracy must be rigorously validated.
In this work, we develop fast simulation models for the BESIII EMC using generative adversarial networks (GANs) and diffusion models. Initial experiments with a baseline conditional GAN show limitated accuracy across a broad range of experimental conditions. To improve performance, we integrate a pre-trained generator designed to produce richer conditional inputs, providing more precise guidance for the generation and leading to a significant improvement. Additionally, we design a conditional diffusion model capable of efficiently simulating multiple track types within a single architecture by injecting a track-type condition. Both models achieve accuracy comparable to Geant4-based simulation. The process is accelerated by up to three orders of magnitude.
A benchmark dataset of single-track events simulated with Gean4 is released for the study and to support further research, covering the full experimental condition space.