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Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques

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Abstract

i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (\(n,\gamma \)) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the \(^{197}\)Au(\(n,\gamma \)) and \(^{56}\)Fe(\(n, \gamma \)) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C\(_6\)D\(_6\) detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of \(\sim \)3 higher detection sensitivity than state-of-the-art C\(_6\)D\(_6\) detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and new analysis methodologies based on Machine-Learning techniques.

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Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ comment: No data is available because no capture yield or cross section was extracted from the experimental analysis.]

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Acknowledgements

This work has been carried out in the framework of a project funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Consolidator Grant project HYMNS, with grant agreement No. 681740). The authors acknowledge support from the Spanish Ministerio de Ciencia e Innovación under grants PID2019-104714GB-C21, FPA2017-83946-C2-1-P, FIS2015-71688-ERC, CSIC for funding PIE-201750I26, and the funding agencies of the participating institutes. We would like to thank the crew at the Electronics Laboratory of IFIC, in particular Manuel Lopez Redondo and Jorge Nácher Arándiga for their excellent and efficient work.

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Authors

Contributions

VB-S: investigation, methodology, formal analysis, data curation, visualization, writing-original draft. JL-M: investigation, methodology, formal analysis, data curation, visualization, writing-original draft. JB-C: investigation, methodology, formal analysis, data curation. DC: investigation. LC: investigation, methodology. IL: software, visualization. CD-P: conceptualization, methodology, supervision, writing-review & editing, funding acquisition, investigation, formal analysis. FC: investigation. AC: investigation. ATS: investigation. VA: investigation. CG: investigation. MAM-C: investigation. TR-G: investigation. MB: investigation. Other co-authors n_TOF Collaboration: investigation, resources, writing review.

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Correspondence to J. Lerendegui-Marco.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Communicated by Wolfram Korten.

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Babiano-Suárez, V., Lerendegui-Marco, J., Balibrea-Correa, J. et al. Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques. Eur. Phys. J. A 57, 197 (2021). https://doi.org/10.1140/epja/s10050-021-00507-7

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  • DOI: https://doi.org/10.1140/epja/s10050-021-00507-7