PoS - Proceedings of Science
Volume 441 - XVIII International Conference on Topics in Astroparticle and Underground Physics (TAUP2023) - Neutrino Physics and Astrophysics
Enhancing Atmospheric Background Reduction using Convolutional Neural Networks in DSNB searches at Super-Kamiokande Gd
S. Samani* and  On behalf of the Super-Kamiokande Collaboration
Full text: pdf
Pre-published on: February 27, 2024
Published on: March 22, 2024
The detection of the Diffuse Supernova Neutrino Background (DSNB) flux will provide invaluable insights into constraining cosmological models, core-collapse dynamics and neutrino properties. The Super-Kamiokande-Gd (SK-Gd) experiment currently exhibits the best sensitivity for discovery due to enhanced neutron tagging capability with 0.011% gadolinium sulfate octahydrate, as per this analysis. While the Inverse Beta Decay (IBD) interaction is identifiable in SK-Gd, the low-energy signal is dominated by atmospheric neutrino backgrounds. This study explores a novel approach to background reduction by leveraging topological features of SK events with the discriminative power of Convolutional Neural Networks (CNNs). Well-established techniques for data preprocessing, event selection and feature extraction are used to train CNNs on IBD and atmospheric Neutral Current (NC) Monte-Carlo (MC) events. Preliminary performance of two CNN models highlights the potential of using machine-learning techniques to improve the DSNB signal efficiency
DOI: https://doi.org/10.22323/1.441.0223
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