Speaker
Description
Modern unfolding techniques pay special attention to
systematic effects, such as imprecisely known or varying properties of the detection process. This necessary, although fairly novel, shift towards the modeling of systematic effects corresponds to a desirable relaxation of assumptions previously imposed by the physicist, so that unfolding can represent the observed data more closely than historical unfolding techniques could. We recently studied this relaxation from a learning-theoretic viewpoint, finding that it demands more computational resources and closer attention from the physicist. Specifically, this relaxation yields only local consistency of unfolding techniques, whereas the historical assumption yields global consistency. These two levels of statistical consistency correspond to two different levels of trustworthiness: methods enjoying global consistency are trustworthy as soon as the (inappropriately strict) historical assumption holds, but not otherwise; methods with only local consistency instead require the physicist to properly handle multi-start optimization, or other heuristics for global non-convex optimization.