2024/01/24 by C. L. Jiang, Sitian Qian, Jiang, Cheng +3 · 2 citations
Computer Science · Medicine · #FOS: Physical sciences #Generative Adversarial Networks and Image Synthesis #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Instrumentation and Detectors (physics.ins-det) #Medical Imaging Techniques and Applications #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.2401.13162
openalex publication_date 2024/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks. Prior applications of the diffusion model for both fast event and detector simulation in high energy physics have shown exceptional performance, providing a viable solution to generate sufficient statistics within a constrained computational budget in preparation for the High Luminosity LHC. However, many of these applications suffer from slow generation with large sampling steps and face challenges in finding the optimal balance between sample quality and speed. The study focuses on the latest benchmark developments in efficient ODE/SDE-based samplers, schedulers, and fast convergence training techniques. We test on the public CaloChallenge and JetNet datasets with the designs implemented on the existing architecture, the performance of the generated classes surpass previous models, achieving significant speedup via various evaluation metrics.