26TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY & NUCLEAR PHYSICS (CHEP2023)

De-Noising CLAS12 Drift Chambers using Convolutional Auto Encoders

Not scheduled

1h

Hampton Roads Ballroom and Foyer Area (Norfolk Waterside Marriott)

Poster Poster Poster Session

Speaker

Gavalian, Gagik (Jefferson Lab)

Description

Modern Nuclear Physics experimental setups run experiments with higher beam intensity resulting in increased noise in detector components
used for particle track reconstruction. Increased uncorrelated signals (noise) result in decreased particle reconstruction efficiency.
In this work, we investigate the usage of Machine Learning, specifically Convolutional Neural Network Auto-Encoders (CAE), for de-noising
raw hits from drift chambers in the CLAS12 detector. As a result of this work we were able to increase tracking efficiency for CLAS12
resulting 80% increase of multi-track reactions outcome at high luminosity. Implemented neural network will allow increasing the
luminosity of the experiment by factor of 3.