Solar Energetic Particle Time Series Analysis with Python

TL;DR

This paper presents Python tools for downloading, visualizing, and analyzing Solar Energetic Particle data from multiple spacecraft missions, facilitating SEP research and space weather studies with accessible workflows and open-source code.

Contribution

It introduces a comprehensive Python package for SEP data analysis, including data retrieval, visualization, and onset time determination, tailored for the current space mission datasets.

Findings

Python functions enable efficient SEP data analysis.

Workflow is suitable for use within Jupyter Notebooks.

Source code is openly available on GitHub.

Abstract

Solar Energetic Particles (SEPs) are charged particles accelerated within the solar atmosphere or the interplanetary space by explosive phenomena such as solar flares or Coronal Mass Ejections (CMEs). Once injected into the interplanetary space, they can propagate towards Earth, causing space weather related phenomena. For their analysis, interplanetary in-situ measurements of charged particles are key. The recently expanded spacecraft fleet in the heliosphere not only provides much-needed additional vantage points, but also increases the variety of missions and instruments for which data loading and processing tools are needed. This manuscript introduces a series of Python functions that will enable the scientific community to download, load, and visualize charged particle measurements of the current space missions that are especially relevant to particle research as time series or dynamic spectra. In addition, further analytical functionality is provided that allows the determination of SEP onset times as well as their inferred injection times. The full workflow, which is intended to be run within Jupyter Notebooks and can also be approachable for Python laymen, will be presented with scientific examples. All functions are written in Python, with the source code publicly available at GitHub under a permissive license. Where appropriate, available Python libraries are used, and their application is described.