Machine Learning for Pulse Shape Analysis of Heavy Ions

Sammanfattning: Most of the Generation IV nuclear reactors designs are intended to operate with a fast neutron spectrum. This necessitates further investigation into nuclear fuel behaviour because fast neutrons yield a higher neutron multiplicity with fission fragments, significantly impacting the criticality assessment of these reactors. Current nuclear research conducted at the VElocity foR Direct particle Identification spectrometer (VERDI) is focused on enhancing our understanding of the relationship between fission fragment excitation energy and the compound nucleus, as well as the correlation between excitation energy and neutron multiplicity. This is achieved through the innovative double energy, double-velocity technique (2E-2v). However, time-of-flight measurements face challenges due to the inherent Plasma Delay Time (PDT) resulting from the interaction between ions and Passivated Implanted Planar Silicon (PIPS) detectors. The interacting between fission fragments and the PIPS detectors results plasma, which in turn creates a disturbance in the electric field. This disturbance causes there to be a delay in the electron-holes moving across the surface, resulting in a delay in the timing signal, the PDT.  The purpose is of this study is to parameterize the PDT and explore the amount of information that can be extracted from the pulse shapes of heavy ions using Deep Neural Networks. Moreover, to delve deeper into this phenomenon, an experiment was conducted at Institut Laue-Langevin (LOHENGRIN), and the data from this experiment serves as the foundation for this study. This research demonstrates that it is possible to achieve an accuracy surpassing the timing resolution available at VERDI using data from a single detector. The developed neural network exhibits robustness, translational invariance, and the ability to generalize well across all ion signals. Interestingly, when trained solely on the pulse shape, excluding pulse height information, the network still attains highly satisfactory accuracy. Indicating that the pulse shape already holds ample information. However, further investigations are necessary to enhance the network's ability to generalize across data from multiple detectors.