New avenue to the Parton Distribution Functions: Self-Organizing Maps

TL;DR

This paper introduces a novel method using Self-Organizing Maps to construct Parton Distribution Functions, offering increased user control and potentially reducing systematic bias compared to traditional neural network approaches.

Contribution

It presents an interactive neural network technique with Self-Organizing Maps for PDF parametrization, enhancing control over systematic biases in the fitting process.

Findings

SOMs effectively cluster PDF samples based on features.

The method allows user interaction to improve fit quality.

Potential reduction in systematic bias compared to standard methods.

Abstract

Neural network 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 selections of stochastically generated PDF samples. The selection criterion for every optimization iteration is based on the features of the clustered PDFs. 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.