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

Understand

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.