  Autonomous experiments are excellent tools to increase the efficiency of
material discovery. Indeed, AI and ML methods can help optimizing valuable
experimental resources as, for example, beam time in neutron scattering
experiments, in addition to scientists' knowledge and experience. Active
learning methods form a particular class of techniques that acquire knowledge
on a specific quantity of interest by autonomous decisions on what or where to
investigate next based on previous measurements. For instance, Gaussian Process
Regression (GPR) is a well-known technique that can be exploited to accomplish
active learning tasks for scattering experiments as was recently demonstrated.
Gaussian processes are not only capable to approximate functions by their
posterior mean function, but can also quantify uncertainty about the
approximation itself. Hence, if we perform function evaluations at locations of
highest uncertainty, the function can be "optimally" learned in an iterative
manner. We suggest the use of log-Gaussian processes, being a natural approach
to successfully conduct autonomous neutron scattering experiments in general
and TAS experiments with the instrument PANDA at MLZ in particular.
