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This article presents the reconstruction of the electromagnetic activity fromelectrons and photons (showers) used in the MicroBooNE deep learning-based lowenergy electron search. The reconstruction algorithm uses a combination oftraditional and deep learning-based techniques to estimate shower energies. Wevalidate these predictions using two $\nu_{\mu}$-sourced data samples:charged/neutral current interactions with final state neutral pions and chargedcurrent interactions in which the muon stops and decays within the detectorproducing a Michel electron. Both the neutral pion sample and Michel electronsample demonstrate agreement between data and simulation. Further, the absoluteshower energy scale is shown to be consistent with the relevant physicalconstant of each sample: the neutral pion mass peak and the Michel energycutoff.

journal of instrumentation

Electromagnetic Shower Reconstruction and Energy Validation with Michel  Electrons and $\pi^0$ Samples for the Deep-Learning-Based Analyses in  MicroBooNE