CaloChallenge Workshop

Session

Diffusion

30 May 2023, 16:30

Villa Mondragone

1. Score-based Generative Models for Calorimeter Shower Simulation

Vinicius Mikuni (LBNL)

30/05/2023, 16:30

Diffusion generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. Three...

3. Denoising Diffusion Models for High Fidelity Calorimeter Simulation

Oz Amram (Fermilab)

30/05/2023, 17:00

Denoising diffusion models have recently become state of the art in the ML community because of their stable training procedure and ability to generate high quality images in reasonable computation times. We employ diffusion models for the task of generating calorimeter showers within the context of the CaloChallenge. Our diffusion models are based on 3D cylindrical convolutional networks,...

11. CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Anatolii Korol (Deutsches Elektronen-Synchrotron (DESY))

30/05/2023, 17:30

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 for the first time directly...

31. Discussion

30/05/2023, 18:00