Conveners
Generative: Diffusion Models
- Johnny Raine (Universite de Geneve (CH))
Simulating particle physics data is a crucial yet computationally expensive aspect of analyzing data at the LHC. Typically, in fast simulation methods, we rely on a surrogate calorimeter model to generate a set of reconstructed objects. This work demonstrates the potential to generate these reconstructed objects in a single step, effectively replacing both the calorimeter simulation and...
Calorimeter response simulation is a critical but computationally consuming part of many physics analyses at the Large Hadron Collider. The simulation time and resource consumption can be effectively reduced by the usage of neural networks. Denosing diffusion models are emerging as the state-of-the-art for various generative tasks ranging from images to sets. We propose a new graph-based...
Diffusion generative models are a recent type of generative models that excel in various tasks, including those in collider physics and beyond. Thanks to their stable training and flexibility, these models can easily incorporate symmetries to better represent the data they generate. In this talk, I will provide an overview of diffusion models' key features and highlight their practical...
Generative machine learning models are a promising avenue to resolve computing challenges by replacing intensive full simulations of particle detectors. We introduce CaloDiffusion, a denoising diffusion model that generates calorimeter showers, trained on the public CaloChallenge datasets. Our algorithm employs 3D cylindrical convolutions that take advantage of symmetries in the underlying...
Given the recent success of diffusion models in image generation, we study their applicability to generating LHC phase space distributions. We find that they achieve percent level precision comparable to INNs. To further enhance the interpretability of our results we quantify our training uncertainty by developing Bayesian versions. In this talk, diffusion models are introduced and discussed...
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint.
This work achieves a major breakthrough in this task by directly generating a...
The simulation of particle interactions with detectors plays a central role in many high energy physics experiments. In the simulation pipeline, the most computationally expensive process is calorimeter shower generation. Looking into the future, as the size and granularity of calorimeters increase and we approach the high luminosity operational phase of the LHC, the severity of the simulation...
