| Article | |
| Title | A Case Study on Deep Learning applied to Capture Cross Section Data Analysis |
| Author(s) | Sanchez-Caballero, Adrian (Madrid, CIEMAT) ; Alcayne, Víctor (Madrid, CIEMAT) ; Cano-Ott, Daniel (Madrid, CIEMAT) ; Mendoza, Emilio (Madrid, CIEMAT) ; Pérez de Rada, Alberto (Madrid, CIEMAT) |
| Collaboration | n_TOF Collaboration |
| Publication | 2023 |
| In: | EPJ Web Conf. 284 (2023) 16001 |
| In: | 15th International Conference on Nuclear Data for Science and Technology, Online, 21 - 29 Jul 2022, pp.16001 |
| DOI | 10.1051/epjconf/202328416001 |
| Subject category | Data Analysis and Statistics ; Computing and Computers |
| Accelerator/Facility, Experiment | CERN PS ; nTOF |
| Abstract | 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 $^{197}$Au and $^{239}$Pu samples suggests that the capture-to-background ratio can be increased 5 times above the standard method. |
| Copyright/License | publication: © 2023-2024 The authors |
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Journalen skapades 2024-11-05, och modifierades senast 2024-11-05