Abstract

A γ-spectroscopy campaign named “ν-Ball” was perfomed at the ALTO facility. A large fraction of the beam time was dedicated to the fast neutron induced fission of two fissioning systems: 232Th and 238U. During the data analysis, it was noticed that the high activity of the natTh was heavily contaminating any coincidence matrices (or cubes) built. This caused the identification of weakly produced fission fragments identification to be almost impossible. It was decided to explore the opportunity opened by new analysis methods based on neural networks algorithms. In this paper, the methods to build an adequate neural network and the results obtained for fission event reconstruction are presented.

Highlights

  • In 2017-2018, the ALTO facility of the “Laboratoire de Physique des 2 infinis Irène JoliotCurie” hosted an experimental campaign using the γ spectrometer named: ⌫-ball

  • The clover germanium detectors were borrowed from the GAMMAPOOL [8] for the duration of the campaign and are placed in two rings of twelve detectors each around 90◦ (75.5◦ and 104.5◦ with respect to the beam axis) to take advantage of their granularity in case of Doppler corrections

  • As mentioned in the previous section, a part of the beam time of the ⌫-Ballcampaign has been dedicated to the coupling with the LICORNE neutron source. It included three experimental projects on fission process studies: one dedicated to the search of fission isomer and spectroscopy in the second potential well, the other two dedicated to study of fission fragments produced by the fast neutron induced fission of 23982U and 23920Th

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Summary

Introduction

In 2017-2018, the ALTO facility of the “Laboratoire de Physique des 2 infinis Irène JoliotCurie” (former “Institut de Physique Nucléaire d’Orsay”) hosted an experimental campaign using the γ spectrometer named: ⌫-ball. Ten coaxial germanium detectors, named “Phase I”, were placed in a ring at backward angles (133.5◦ with respect to the beam axis) and 16 cm from the center of the sphere to the BGO shield entrance window. The clover germanium detectors were borrowed from the GAMMAPOOL [8] for the duration of the campaign and are placed in two rings of twelve detectors each around 90◦ (75.5◦ and 104.5◦ with respect to the beam axis) to take advantage of their granularity in case of Doppler corrections. A separate configuration involving the coupling of ⌫-Ballwith four clusters of the PARIS [9,10,11] array was performed For this setup all the LaBr3 detectors were replaced by 34 PARIS phoswitch detectors almost completely covering the available solid angle in the forward direction ( ⇠30%). An energy resolution of 2.7% and an photopeak efficiency up to 1.5% (depending on the crystals positions) was measured at 662.7 keV

The data acquisition system
Neural networks
Training Procedure
Neural network quality
Findings
Conclusion
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