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Accurate measurement of the standard 235U(n,f) cross section from thermal to 170 keV neutron energy
(2020)
An accurate measurement of the 235U(n,f) cross section from thermal to 170 keV of neutron energy has recently been performed at n_TOF facility at CERN using 6Li(n,t)4He and 10B(n,α)7Li as references. This measurement has been carried out in order to investigate a possible overestimation of the 235U fission cross section evaluation provided by most recent libraries between 10 and 30 keV. A custom experimental apparatus based on in-beam silicon detectors has been used, and a Monte Carlo simulation in GEANT4 has been employed to characterize the setup and calculate detectors efficiency. The results evidenced the presence of an overestimation in the interval between 9 and 18 keV and the new data may be used to decrease the uncertainty of 235U(n,f) cross section in the keV region.
The Cosmological Lithium Problem refers to the large discrepancy between the abundance of primordial 7Li predicted by the standard theory of Big Bang Nucleosynthesis and the value inferred from the so-called “Spite plateau” in halo stars. A possible explanation for this longstanding puzzle in Nuclear Astrophysics is related to the incorrect estimation of the destruction rate of 7Be, which is responsible for the production of 95% of primordial Lithium. While charged-particle induced reactions have mostly been ruled out, data on the 7Be(n,α) and 7Be(n,p) reactions are scarce or completely missing, so that a large uncertainty still affects the abundance of 7Li predicted by the standard theory of Big Bang Nucleosynthesis. Both reactions have been measured at the n_TOF facility at CERN, providing for the first time data in a wide neutron energy range.
The radiative capture cross section of 238U is very important for the developing of new reactor technologies and the safety of existing ones. Here the preliminary results of the 238U(n,γ) cross section measurement performed at n_TOF with C6D6 scintillation detectors are presented, paying particular attention to data reduction and background subtraction.