New avenue to the parton distribution functions: Self-organizing maps

Neural network algorithms have been recently applied to construct parton distribution function (PDF) parametrizations which provide an alternative to standard global fitting procedures. In this exploratory study we propose a technique using self-organizing maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially ordered neurons. Our SOMs are trained on selections of stochastically generated PDF samples. The selection criterion for every optimization iteration is based on the features of the clustered PDFs. Our goal is a fitting procedure that, at variance with the standard neural network approaches, will allow for an increased control of the systematic bias by enabling user interaction in the various stages of the process.