29 November 2021 to 3 December 2021
Virtual and IBS Science Culture Center, Daejeon, South Korea
Asia/Seoul timezone

Denoising Convolutional Networks to Accelerate Detector Simulation

contribution ID 682
Not scheduled
20m
Orange (Gather.Town)

Orange

Gather.Town

Poster Track 2: Data Analysis - Algorithms and Tools Posters: Orange

Speakers

CMS Collaboration Kevin Pedro (Fermi National Accelerator Lab. (US))

Description

The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades associated with next-generation facilities such as the High Luminosity LHC. We explore the viability of regression-based machine learning (ML) approaches using convolutional neural networks (CNN) to "denoise" faster, lower-quality detector simulations, augmenting them to produce a higher-quality final result with a reduced computational burden. The denoising CNN works in concert with classical detector simulation software rather than replacing it entirely, increasing its reliability compared to other ML approaches to simulation. We obtain promising results from a prototype based on photon showers in the CMS electromagnetic calorimeter. Future directions are also discussed.

Speaker time zone Compatible with America

Primary author

Presentation materials