Identification and Classification of Corrupted Signals for the Neutrinos Angra Experiment

The Neutrinos Angra Experiment aims at monitoring nuclear plants by detecting antineutrino particles coming out from its fuel burn-up process. The Neutrinos Angra Collaboration developed a fully-equipped detector to accomplish this task. It is currently in operation on the surface and next to the dome of the Angra II nuclear reactor. Selecting antineutrinos events on the surface is a challenge task due to the high level of background noise produced by cosmic ray particles. One of the main parameters used to select antineutrino events is the number of photons acquired by the detector's sensors. This quantity is estimated based on the signals generated at the output of the readout electronics. If any of those signals is corrupted, the estimation fails, compromising the Experiment performance. This work proposes a study of the performance of some classifier algorithms applied to identify corrupted signals for the Neutrinos Angra Experiment. If corrupted signals can be identified, they could go through a recovering process in order to improve the Experiment's events-selection algorithms.