  In this article, we present the results of using Convolutional Auto-Encoders
for de-noising raw data for CLAS12 drift chambers. The de-noising neural
network provides increased efficiency in track reconstruction and also improved
performance for high luminosity experimental data collection. The de-noising
neural network used in conjunction with the previously developed track
classifier neural network \cite{Gavalian:2022hfa} lead to a significant track
reconstruction efficiency increase for current luminosity
($0.6\times10^{35}~cm^{-2}~sec^{-1}$ ). The increase in experimentally measured
quantities will allow running experiments at twice the luminosity with the same
track reconstruction efficiency. This will lead to huge savings in accelerator
operational costs, and large savings for Jefferson Lab and collaborating
institutions.
