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The experimental setup of the new measurement of 239Pu fission and capture cross-section in the n_TOF time-of-flight facility at CERN is presented. The measurement aims to address the needs and demands of nuclear data users. The experiment incorporates an innovative fast Fission Fragment Detector and the n_TOF Total Absorption Calorimeter, enabling the implementation of the fission tagging technique. Preliminary results exhibit the robust performance of the detector systems, along with the high quality of the new 239Pu samples. These samples were exclusively produced for this measurement by the European Commission's Joint Research Centre in Geel. [ABSTRACT FROM AUTHOR]

A good data analysis of neutron cross section measurements is necessary for generating high quality and reliable nuclear databases. Artificial intelligence techniques, and in particular deep learning, have proven to be very useful for pattern recognition and data analysis, and thus may be used in the field of experimental nuclear physics. In this publication, we train a neural network in order to improve the capture-to-background ratio of neutron capture data of measurements performed in the time-of-flight facility n_TOF at CERN with the so-called Total Absorption Calorimeter. The evaluation of this deep learning-based method on accurate Monte Carlo simulated measurements with 197Au and 239Pu samples suggests that the capture-to-background ratio can be increased 5 times above the standard method. [ABSTRACT FROM AUTHOR]