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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 Karmakar^1,4,5, Frederik Uhlemann², Heinrich Wilsenach³, Anikesh Pal⁴, Christoph Scheidenberger^1,2, G. Anil Kumar⁵, Mohit Tyagi⁶, Timo Dickel^1,2, 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