Göttingen 2025 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

T: Fachverband Teilchenphysik

T 53: Neutrino Astronomy III

T 53.7: Vortrag

Mittwoch, 2. April 2025, 17:45–18:00, VG 1.105

ML discrimination of atmospheric neutrinos for DSNB detection in JUNO — •David Maksimovic¹, Daniel Tobias Schmid¹, Dhaval J. Ajana², Michael Wurm¹, Marcel Büchner¹, Arshak Jafar¹, George Parker¹, Oliver Pilarczyk¹, and Tim Charisse¹ — ¹Johannes Gutenberg-University Mainz, Institute of Physics — ²Department of Physics, Florida State University, Tallahassee, FL 32306, USA

The detection and analysis of the Diffuse Supernova Neutrino Background (DSNB) pose a significant challenge in neutrino astronomy, primarily due to backgrounds mimicking the Inverse Beta Decay (IBD) signature events. The Jiangmen Underground Neutrino Observatory (JUNO) uses a liquid scintillator to detect these neutrinos, especially challenged by Neutral-Current (NC) interactions of atmospheric neutrinos in the 12 to 30 MeV range. In this talk, we introduce novel methods employing 3D Convolutional Neural Networks (3D CNNs) and Convolutional LSTMs (ConvLSTMS) for better discrimination of DSNB events from these backgrounds. These techniques analyses time-sequenced data from photomultiplier tube (PMT) hit patterns, arranged in frames like a movie, capturing the spatial-temporal dynamics of particle interactions. Simulation studies within the JUNO detector environment show promising background reduction capabilities.

Keywords: Machine Learning; Diffuse Supernova Neutrino Background; JUNO