Uncertainty Quantification for Monte Carlo simulations - Paper 18
Uncertainty quantification (UQ) generally speaking refers to a wide variety of specific problems, also pertaining different scientific areas: then studies on Uncertainty Quantification involve many different methodologies, techniques and even terminologies: to be specific we are interested in studying how uncertainties on input physical data needed for Monte Carlo simulations transfer into uncertainties on the physical output of the simulation. We mention that other sources of uncertainty are be present in a simulation environment: they can be ascribed either to model inadequacy (for instance when one neglects atomic lattice at very low energies) or to the numerical procedure involved: for the case of MC simulation the latter corresponds to the statistical error affecting the simulation itself. So in general parameter uncertainties are mixed with other kind of uncertainties. The problem then remains seemingly quite complicated, even in this more specific domain: moreover we should better specify the meaning of the rather undetermined words physical data needed for Monte Carlo simulation. In fact they may refer both those data needed to specify the experimental setup that is going to be simulated - like, e.g., geometrical or composition data, but including also some external conditions applied to the system, like temperature, pressure, electromagnetic fields and so on - and physical data - cross sections or often physical models - required to describe particle transport. All these data are generally affected by uncertainties, which through the process of simulation transfer into uncertainty on its physical outcome. Then, from the MC user's own perspective, input data refers both to those that are under his direct experimental control and to many others, whose values and uncertainties will affect the result of the simulation. In this contribution we will summarize previous results, together with some new ones. (authors)