Information Criteria for Selecting Parton Distribution Function Solutions

In data-driven determination of Parton Distribution Functions (PDFs) in global QCD analyses, uncovering the true underlying distributions is complicated by a highly convoluted inverse problem. The determination of PDFs can be understood as the inference of a function supported on $[0,1]$, a problem that admits multiple acceptable solutions. An ensemble of solutions exists that pass all standard goodness-of-fit criteria. In this paper, we propose algorithms for the classification, clustering, and selection of solutions to the determination of PDFs, or any functions on $[0,1]$, based on the characterization of their shape. We explore information-theoretic based (R\'enyi entropy and divergence) and optimal-transport based (Wasserstein distance) criteria. In particular, we advocate for the use of the R\'enyi entropy as an {\it absolute} estimator per solution, as opposed to {\it relative} estimators that compare solutions pairwise. We show that the R\'enyi entropy can characterize the space of solutions {\it w.r.t.} the PDF shapes. Paired with the identification of the optimal combination of solutions via Pareto fronts, it provides a plausible and minimalist selection algorithm. Moreover, R\'enyi entropy proves versatile for use in clustering applications.