Denoising Diffusion to Accelerate Detector Simulation
Description
This program aims to develop generative models for quickly simulating showers of particles in calorimeters for LHC experiments
Detailed example
The AI system outputs simulated detector hits (energy deposits) in one or more subdetectors of the particle physics experiment.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
This effort is exploring generative AI to replace costly detector simulation. This would enable faster, more accurate simulation, accelerating and enhancing scientific results and allowing easier use of GPU coprocessors at HPC centers.
Audit / financial statement impact
The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety
Controls / human review
ATO: No; PIA: Not published
Data needed
research datasets from scientific experiments