Interpreting Parton Distributions with Shapley Values
We show that Shapley values can be used to trace how individual parton distributions (PDFs) shape the theory predictions for high-energy observables computed from them. This provides a tool for assessing the impact of data on PDFs when determining them, and the impact of PDF uncertainties when using the PDFs to compute collider observables. The Shapley value is computed by treating the regression of PDFs from data as a cooperative game. The PDFs are the players, and the reward is the likelihood ($\chi^2$) that characterizes the agreement between data and the predictions obtained from a given PDF, with theory and methodology held fixed. The method is agnostic to the way PDFs have been determined in the first place: for PDFs determined with a black-box AI model it may be used in order to explain the behavior of the model, and for PDFs determined using a fixed parametrization it may be used in order to expose the features and potential limitations of the parametrization. We find that the method recovers known expectations about which data constrain which PDFs in a global fit, while placing them on a more quantitative footing. We demonstrate its effectiveness in two ways. We uncover an unexpected loss of sensitivity of the gluon PDF at intermediate $x$, with potential implications for BSM searches and the gluon fusion Higgs cross section. We also show that the method can be used to improve the hyperparameter optimization procedure currently used by the NNPDF collaboration.