  When electrons with energies of $O(100)$ MeV pass through a liquid argon time
projection chamber (LArTPC), they deposit energy in the form of electromagnetic
showers. Methods to reconstruct the energy of these showers in LArTPCs often
rely on the combination of a clustering algorithm and a linear calibration
between the shower energy and charge contained in the cluster. This
reconstruction process could be improved through the use of a convolutional
neural network (CNN). Here we discuss the performance of various CNN-based
models on simulated LArTPC images, and then compare the best performing models
to a typical linear calibration algorithm. We show that the CNN method is able
to address inefficiencies caused by unresponsive wires in LArTPCs and
reconstruct a larger fraction of imperfect events to within 5% accuracy
compared with the linear algorithm.
