Speaker
Description
The CEPC is a proposed high luminosity e+e− collider designed for precision measurements of the Higgs, W, and Z bosons. Its reference detector incorporates a long bar crystal ECAL, which employs long, narrow crystal bars arranged in orthogonal layers to deliver fine 3D shower imaging and excellent compatibility with Particle Flow reconstruction. [1]
For CEPC physics analyses, large volumes of simulated data are essential. Calorimeter simulation is by far the most CPU intensive component of the CEPC detector simulation, accounting for roughly 80% of the total simulation budget. Consequently, the development of fast simulation techniques is a critical R&D priority.
Our work is inspired by CERN’s CaloDiT-2 [2], which develops a fast simulation framework based on the Diffusion Transformer (DiT). Our implementation, named Voxel Diffusion Transformer for Calorimeter (VoDiT4CAL), is built using PyTorch [3] and Lightning [4]. Building on the design principles of CaloDiT-2, VoDiT4CAL introduces two key enhancements:
Enhanced Local Spatial Modelling: VoDiT4CAL incorporates PixelDiT [5] layer to better capture local spatial correlations, which reduces the DiT depth and significantly lowers computational cost.
Enhanced Energy Modelling: VoDiT4CAL adds energy prediction head and dynamically redistributes energy across voxels.
Testing shows that VoDiT4CAL accurately reproduces key photon shower distributions across incident energies from 0.25 GeV to 100 GeV, meeting CEPC physics precision requirements. This contribution also presents a detailed report on distillation(for accelerating inference [6]), its impact on physics performance, and the practical speedup achieved after integrating VoDiT4CAL into the official CEPC software framework.
[1] Souvik Priyam Adhya et al. “CEPC Technical Design Report - Reference Detector”. In: (Oct. 2025). arXiv: 2510.05260 [hep-ex].
[2] Piyush Raikwar et al. “A Generalisable Generative Model for Multi-Detector Calorimeter Simulation”. In: (Sept. 2025). arXiv: 2509.
07700 [physics.ins-det].
[3] Adam Paszke et al. “PyTorch: An Imperative Style, High-Performance Deep Learning Library”. In: Advances in Neural Information
Processing Systems 32. Curran Associates, Inc., 2019, pp. 8024–8035. URL: http://papers.neurips.cc/paper/9015-pytorch-
an-imperative-style-high-performance-deep-learning-library.pdf.
[4] William Falcon and the PyTorch Lightning team. PyTorch Lightning. 2024. DOI: 10.5281/zenodo.13254264. URL: https://doi.
org/10.5281/zenodo.13254264.
[5] Yongsheng Yu et al. “PixelDiT: Pixel Diffusion Transformers for Image Generation”. In: arXiv preprint arXiv:2511.20645 (2025).
[6] Kaiwen Zheng et al. “Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency”. In: ArXiv abs/2510.08431
(2025). URL: https://api.semanticscholar.org/CorpusID:281950486.
