Dr Lisa Morrison

Monte Carlo simulations are widely used in nuclear physics to model experimental systems. In cases where there are significant unknown quantities, such as energies of states, an iterative process of simulating and fitting is often required to describe experimental data. We describe a Bayesian approach to fitting experimental data, designed for data from a 12Be(d,p) reaction measurement, using simulations made with GEANT4. Q-values from the 12C(d,p) reaction to well-known states in 13C are compared with simulations using BayesOpt. The energies of the states were not included in the simulation to reproduce the situation for 13Be where the states are poorly known. Both cases had low statistics and significant resolution broadening owing to large proton energy losses in the solid deuterium target. Excitation energies of the lowest three excited states in 13C were extracted to better than 90 keV, paving a way for extracting information on 13Be.

The first low-energy Coulomb-excitation measurement of the radioactive, semi-magic, two proton -hole nucleus 206Hg, was performed at CERN's recently-commissioned HIE-ISOLDE facility. Two gamma rays depopulating low-lying states in 206Hg were observed. From the data, a reduced transition strength B(E2; 2+1 -> 0+1 ) = 4.4(6) W.u. was determined, the first such value for an N = 126 nucleus south of 208Pb, which is found to be slightly lower than that predicted by shell-model calculations. In addition, a collective octupole state was identified at an excitation energy of 2705 keV, for which a reduced B(E3) transition probability of 30+10 -13 W.u. was extracted. These results are crucial for understanding both quadrupole and octupole collectivity in the vicinity of the heaviest doubly-magic nucleus 208Pb, and for benchmarking a number of theoretical approaches in this key region. This is of particular importance given the paucity of data on transition strengths in this region, which could be used, in principle, to test calculations relevant to the astrophysical r-process.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.