Benito-Calviño, GuillermoGarcía-Olivares, JavierLlanes Estrada, Felipe José2024-04-082024-04-082023-08Benito-Calviño, G., García-Olivares, J., & Llanes-Estrada, F. J. (2023). Information entropy and fragmentation functions. Nuclear Physics A, 1036, 122670.0375-947410.1016/j.nuclphysa.2023.122670https://hdl.handle.net/20.500.14352/1028272023 Acuerdos transformativos CRUESeveral groups have recently investigated the flow of information in high-energy collisions, from the entanglement entropy of the proton yielding classical Shannon entropy of its parton distribution functions (pdfs), through jet splitting generating entropy, to the entropy distribution in hadron decays. Lacking in the literature is a discussion of the information entropy of fragmentation functions (FFs) in the instances where they can be considered as probability distributions, and we here provide it. We find that this entropy is a single, convenient number to characterize future progress in the extraction of fragmentation functions. We also deploy the related Kullback-Leibler divergence between two distributions to assess existing relations among FFs and parton distribution functions (pdfs) such as that of Barone, Drago and Ma. From a couple of current parametrizations of FFs, we do not find supporting empirical evidence for the relation, although it is possible that FFs and pdfs have similar power-laws near the x = 1 endpoint.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal article1873-1554https://www.sciencedirect.com/science/article/pii/S0375947423000738https://arxiv.org/abs/2209.13225open access51-72Fragmentation functionsParton distribution functionsShannon entropyClassical information entropyKullback-Leibler divergenceBarone-Drago-Ma relationFísica-Modelos matemáticos2212 Física Teórica