Source separation techniques for characterising cosmic ray transients from neutron monitor networks

TL;DR

This paper introduces a statistical framework for analyzing neutron monitor network data to better characterize cosmic ray transients, improve data quality, and interpret spatial coherence in particle flux variations.

Contribution

It presents a novel statistical method for source separation and data analysis in neutron monitor networks, enhancing cosmic ray transient characterization.

Findings

Effective data gap filling demonstrated

Trend removal improves signal clarity

Framework enhances understanding of cosmic ray flux variations

Abstract

The analysis of weak variations in the energetic particle flux, as detected by neutron or muon monitors, can often be considerably improved by analysing data from monitor networks and thereby exploiting the spatial coherence of the flux. We present a statistical framework for carrying out such an analysis and discuss its physical interpretation. Two other applications are also presented: filling data gaps and removing trends. This study focuses on the method and its various uses.