  This article presents the reconstruction of the electromagnetic activity from
electrons and photons (showers) used in the MicroBooNE deep learning-based low
energy electron search. The reconstruction algorithm uses a combination of
traditional and deep learning-based techniques to estimate shower energies. We
validate these predictions using two $\nu_{\mu}$-sourced data samples:
charged/neutral current interactions with final state neutral pions and charged
current interactions in which the muon stops and decays within the detector
producing a Michel electron. Both the neutral pion sample and Michel electron
sample demonstrate agreement between data and simulation. Further, the absolute
shower energy scale is shown to be consistent with the relevant physical
constant of each sample: the neutral pion mass peak and the Michel energy
cutoff.
