@misc{10481/73735,
year = {2022},
month = {3},
url = {http://hdl.handle.net/10481/73735},
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.},
organization = {Fermi Research Alliance, LLC (FRA)	DE-AC02-07CH11359},
organization = {United States Department of Energy (DOE)		
National Science Foundation (NSF)},
organization = {Swiss National Science Foundation (SNSF)},
organization = {European Commission},
organization = {Science and Technology Facilities Council (STFC) , part of the United Kingdom Research and Innovation},
organization = {Royal Society of London},
organization = {European Union's Horizon 2020 Marie Sklodowska-Curie Actions},
organization = {Albert Einstein Center for Fundamental Physics, Bern, Switzerland},
publisher = {Institute of Physics},
keywords = {Neutrino detectors},
keywords = {Noble liquid detectors (scintillation, ionization, double-phase)},
keywords = {Time projection Chambers (TPC)},
keywords = {Pattern recognition},
keywords = {Cluster finding},
keywords = {Calibration and fitting methods},
title = {Electromagnetic shower reconstruction and energy validation with Michel electrons and pi(0) samples for the deep-learning-based analyses in MicroBooNE},
author = {Abratenko, P. and García Gámez, Diego and MicroBooNE Collaboration},
}