"Obtaining Parton Distribution Functions from Self-Organizing Maps"
Dr. Heli Honkanen
University of Virginia
Abstract
Neural nerwork algorithms have been recently applied to construct Parton
Distribution Function (PDF) parametrizations which provide an alternative
to standard global fitting procedures. We propose a technique based on an
interactive neural network algorithm 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 progressively
narrower selections of stochastically generated PDF samples. The selection
criterion is that of convergence towards a neighbourhood of the
experimental data. Our main goal is to provide a fitting procedure that,
at variance with the standard neural network approaches, allows for an
increased control of the systematic bias by enabling user interaction in
the various stages of the process.