PC-Droid: Faster diffusion and improved quality for particle cloud generation

Abstract

Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds. By leveraging a new diffusion formulation, studying more recent integration solvers, and training on all jet types simultaneously, we are able to achieve state-of-the-art performance for all types of jets across all evaluation metrics. We study the trade-off between generation speed and quality by comparing two attention based architectures, as well as the potential of consistency distillation to reduce the number of diffusion steps. Both the faster architecture and consistency models demonstrate performance surpassing many competing models, with generation time up to two orders of magnitude faster than PC-JeDi and three orders of magnitude faster than Delphes.

arXiv comment21 pages, 8 tables, 13 figures

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Cited by in corpus (19)

• The MadNIS Reloaded
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Normalizing Flows for High-Dimensional Detector Simulations
• Calorimeter shower superresolution
• Differentiable MadNIS-Lite
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments
• PIPPIN: Generating variable length full events from partons
• Calibrating Bayesian Generative Machine Learning for Bayesiamplification
• Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
• Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
• Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
• Ultra fast, event-by-event heavy-ion simulations for next generation experiments
• Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion
• Extrapolating Jet Radiation with Autoregressive Transformers
• Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
• Variational inference for pile-up removal at hadron colliders with diffusion models