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

TL;DR

This paper details a method combining traditional and deep learning techniques for reconstructing electromagnetic showers in MicroBooNE, validated with data samples involving Michel electrons and $^0$ mesons, ensuring accurate energy estimation.

Contribution

It introduces a novel reconstruction algorithm that integrates deep learning with traditional methods for electromagnetic shower energy estimation in MicroBooNE.

Findings

Data and simulation show good agreement in shower energy reconstruction.

The energy scale aligns with physical constants like the neutral pion mass.

Validation with Michel electrons confirms the method's accuracy.

Abstract

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.