Speaker
Description
The accurate simulation of particle showers in collider detectors remains a critical bottleneck for high-energy physics research. Current approaches face fundamental limitations in scalability when modeling the complete shower development process.
Deep generative models offer a promising alternative, potentially reducing simulation costs by orders of magnitude. This capability becomes increasingly vital as upcoming particle physics experiments are expected to produce unprecedented volumes of data.
We present a novel domain adaptation framework employing state-of-the-art deep generative models to generate high-fidelity point-cloud representations of electromagnetic particle showers. Using transfer learning techniques, our approach adapts simulations across diverse electromagnetic calorimeter geometries with exceptional data efficiency, thereby reducing training requirements and eliminating the need for a fixed-grid structure.
The results demonstrate that our method can achieve high accuracy while significantly reducing data and computational demands, offering a scalable solution for next-generation particle physics simulations.
We also investigate the stability of generative models under iterative training, a process in which models are retrained on their own generated data. While model collapse has been observed in large language models and variational autoencoders for natural image generation, its implications for high-energy physics remain unexplored. We study this phenomenon in the context of particle shower simulation, using normalizing flows and diffusion models.
Significance
To our knowledge, this is the first time a domain adaptation framework for electromagnetic shower simulation has been proposed. This framework could significantly reduce the amount of data and time needed to train a generative model. Since training data must be simulated with full Monte Carlo and training generative models is computationally expensive, this could significantly reduce the computing resources needed to train a generative model for shower simulation.
| Experiment context, if any | International Large detector; https://www.ilcild.org |
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