14 MeV neutron irradiation data gamma spectroscopy analysis and validation automation

14 MeV neutron irradiation data gamma spectroscopy analysis and validation automation

14 MeV neutron irradiation data gamma spectroscopy analysis and validation automation 150 150 UKAEA Opendata

14 MeV neutron irradiation data gamma spectroscopy analysis and validation automation

An important area of research required for fusion reactor design is the study of materials under high energy neutron irradiation. Deuterium-Tritium (D-T) reactions release 14.1 MeV neutrons and material studies of such high energy neutrons focusing on transmutation and activation are paramount for fusion tokamak devices such as ITER and DEMO. These reactors expect to experience neutron fluxes of the order of 1018 neutrons m-2s- 1, and quantities such as nuclear heating, neutron damage and transmutation-induced radioactivity are crucial for their design and operation. Currently no facilities exist which offer the ability to expose materials to neutron fluxes and energies equivalent to those predicted for fusion. Whilst plans are underway to develop a DEMO Oriented Neutron Source (DONES) to tackle this issue, little data is available for material exposure under 14 MeV neutron irradiation.

The ASP facility based at Aldermaston in the UK, uses a deuteron accelerator to bombard a tritium-loaded target and generate 14 MeV-neutron emission rates of up to 2.5 × 1011 s-1. Whilst these fluences are not high enough to perform material damage studies, the facility can be used to generate useful data for the improvement and validation of nuclear reaction cross sections relevant to neutron-induced activation responses. In our work, ASP has been used to irradiate a range of thin foil samples, resulting in the generation of radionuclides via neutron-induced reactions. Post-irradiation, various high-resolution gamma spectroscopy measurements using a high-purity germanium detector were used to measure the characteristic gamma energy emissions from the radioactive species.

Several campaigns have been conducted at the ASP facility, covering several hundred foil samples, resulting in the collection of over 11,000 raw gamma spectra data sets. Previous studies have been conducted involving subsets of the data, providing validation and refinements in nuclear data, such as half-life measurements, and integral cross section measurements, which are presented elsewhere. However, holistic utilisation of the full data set towards validation has not been fully realised since the analyses to date has been largely driven by human effort via a series of codes and scripts. Such approaches are not only prone to human bias and error but are time-consuming to perform and repeat.

This work presents a new approach in which an automated infrastructure using database technologies and machine learning methods is employed to filter experimental data, process the raw spectra, identify radioisotopes, and validate against inventory codes, such as FISPACT- II and associated nuclear data libraries, in a purely automatic fashion. In addition to the standard methods of radio-isotope identification, novel methods using Artificial Neural Networks (ANN) and classification algorithms are applied to the data set and are compared. An overview of the ASP facility, including the accelerator, the irradiation cell, and the fast sample extraction system and future experimental plans are also briefly presented.

Work supported by RCUK [grant number EP/P012450/1] and the Euratom research and training programme.

PHYSOR 2020, University of Cambridge, United Kingdom, 29 March - 2 April 2020
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