Speaker
Description
Super Tau Charm Facility(STCF), as a new-generation high-luminosity collider experiment, places higher demands on the generation of large-scale Monte Carlo samples.MC simulation, especially for electromagnetic calorimeter(ECAL), requires substantial computational resources. Traditional Geant4 approach simulates the secondary particles and interactions of electromagnetic showers in ECAL. But with the development of machine learning, generative adversarial network(GAN) can directly generate information such as energy deposition maps from the particles’ inject conditions. Applying GAN to ECAL fast simulation in STCF experiment can significantly reduce computational cost while maintaining high accuracy.
This talk presents the motivation and methodology of developing and optimizing GAN for ECAL fast simulation in STCF, and provides a comparison between Geant4 and GAN in terms of simulation results and computational efficiency.
