Use of Bayesian Optimization to understand the structure of nuclei
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 12 Be
(d, p) reaction measurement, using simulations made with GEANT4. Q-values from the 12 C
(d, p) reaction to well-known states in 13 C are compared with simulations using BayesOpt …
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 12 Be
(d, p) reaction measurement, using simulations made with GEANT4. Q-values from the 12 C
(d, p) reaction to well-known states in 13 C are compared with simulations using BayesOpt …