Köln 2025 – wissenschaftliches Programm
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HK: Fachverband Physik der Hadronen und Kerne
HK 27: Computing I
HK 27.2: Vortrag
Mittwoch, 12. März 2025, 14:15–14:30, SR Exp1A Chemie
Neutron-Gamma Multiplicity and Discrimination in 252Cf Fission: GEANT4 Simulations and Machine Learning Approaches — •Annesha Karmakar1,4,5, Frederik Uhlemann2, Heinrich Wilsenach3, Anikesh Pal4, Christoph Scheidenberger1,2, G. Anil Kumar5, Mohit Tyagi6, Timo Dickel1,2, and Wolfgang.R Plass2 — 1GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany — 2II. Physikalisches Insti- tut, Heinrich-Buff-Ring 14, Giessen, Germany — 3FRS Ion Catcher Collaboration, Tel Aviv University, Isreal — 4Department of Mechanical Engineering, Indian Institute of Technology Kanpur, India — 5Department of Physics, Indian Institute of Technology Roorkee, India — 6Technical Physics Division, Bhabha Atomic Research Centre, Mumbai ,India
This study examines neutron and gamma-ray distributions from 252Cf fission, linking them to specific prompt release events using GEANT4 and GEF simulations. The neutron energy spectrum peaks at 2 MeV and extends up to 15 MeV, with event-by-event correlations analysed using plastic scintillation detectors. Pulse Shape Discrimination (PSD) is crucial for accurate neutron-gamma identification, traditionally achieved through charge integration methods but requiring manual optimization. Machine learning techniques, such as deep neural networks (DNN) and convolutional neural networks (CNN), provide faster and more reliable PSD, particularly at low energies, enhancing our understanding of neutron-gamma multiplicity.
Keywords: Spontaneous Fission; GEANT4; GEF; Plastic Scintillation Detector; Machine Learning