Principal Components and Independent Component Analysis of Solar and Space Data

TL;DR

This paper applies PCA and ICA to solar and space data to identify global patterns, structures, and preferred longitudes, revealing correlated behaviors during solar cycle 23.

Contribution

It introduces the combined use of PCA and ICA for analyzing solar and space data, highlighting their effectiveness in detecting coherent structures and independent modes.

Findings

PCA identified coherent structures in interplanetary magnetic fields.

ICA revealed maximally independent modes in solar data.

Rotations of sector structures vary together during solar cycle 23.

Abstract

Principal Components Analysis (PCA) and Independent Component Analysis (ICA) are used to identify global patterns in solar and space data. PCA seeks orthogonal modes of the two-point correlation matrix constructed from a data set. It permits the identification of structures that remain coherent and correlated or which recur throughout a time series. ICA seeks for maximally independent modes and takes into account all order correlations of the data. We apply PCA to the interplanetary-magnetic-field polarity near one AU and to the 3.25R source-surface fields in the solar corona. The rotations of the two-sector structures of these systems vary together to high accuracy during the active interval of solar cycle 23. We then use PCA and ICA to hunt for preferred longitudes in northern hemisphere, Carrington maps of magnetic fields.