Speaker
Description
The Neutrino Elastic Scattering Observation with NaI(Tl) (NEON) experiment aims to observe coherent elastic neutrino-nucleus scattering (CEvNS) using reactor electron antineutrinos. The experiment employs six NaI(Tl) detectors with a total mass of 16.7 kg. To detect CEvNS events, it is essential to achieve a low-energy threshold of 200 eV, which corresponds to 5 photoelectrons, assuming a light yield of 25 photoelectrons/keV in the NaI(Tl) detectors. To meet this requirement, a deep learning-based event selection strategy is being developed using a ResNet-based convolutional neural network (CNN). To ensure sufficient training statistics for the signal sample, a highly accurate waveform simulation has also been developed and is actively utilized in the training process. This poster presents an overview of the NEON experiment’s event selection procedure, including the deep learning methodology and the waveform simulation framework that supports it.
