Fusion plasmas produce neutrons and by measuring the neutron emission the fusion power can be inferred. Accurate neutron yield measurements are paramount for the safe and efficient operation of fusion experiments and, eventually, fusion power plants. Neutron measurements are an essential part of the diagnostic system at large fusion machines such as JET and ITER. At JET, a system of activation foils provides the absolute calibration for the neutron yield determination. The activation system uses the property of certain nuclei to emit radiation after being excited by neutron reactions. A sample of suitable nuclei is placed in the neutron flux close to the plasma and after irradiation the induced radiation is measured. Knowing the neutron activation cross section one can calculate the time-integrated neutron flux at the sample position. To relate the local flux to the total neutron yield, the spatial flux response has to be identified. This describes how the local neutron emission affects the flux at the detector. The required spatial flux response is commonly determined using neutron transport codes, e.g., MCNP. Nuclear data is used as input both in the calculation of the spatial flux response and when the flux at the irradiation site is inferred. Consequently, high quality nuclear data is essential for the proper determination of the neutron yield and fusion power. However, uncertainties due to nuclear data are generally not fully taken into account in today’s uncertainty analysis for neutron yield calibrations using activation foils. In this paper, the neutron yield uncertainty due to nuclear data is investigated using the so-called Total Monte Carlo Method. The work is performed using a detailed MCNP model of JET fusion machine. In this work the uncertainties due to the cross sections and angular distributions in JET structural materials, as well as the activation cross sections, are analyzed. It is shown that a significant contribution to the neutron yield uncertainty can come from uncertainties in the nuclear data.
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