Particle Tracking Classification in the CONNIE Experiment - Santos, Sara Mirthis Dantas dos; Nasteva, Irina; Costa, Paula Dornhofer Paro (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2025.14311, 2025)
The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is a particle physics experiment located at the Angra 2 Nuclear Reactor. Using silicon Skipper-CCDs, CONNIE aims to identify and investigate coherent elastic neutrino-nucleus scattering (CEνNS). However, background particles from various sources, combined with the large volume of unlabeled data, present significant challenges, making statistical analysis difficult. To address these challenges, this work investigates and implements machine learning and image processing methodologies to classify distinct CONNIE events. A labeled dataset of real experimental events will also be created using the Annotation Redundancy with Targeted Quality Assurance method. This approach involves multiple annotators labeling the same data, enabling comparison to identify discrepancies, refine labeling accuracy, and enhance dataset reliability.