awesome-helio

Solar physics link collection.

Digital library portal for researchers in astronomy and physics.

Partnership for space science and space weather models.

Useful materials for different missions.

Space-related research at the FHNW Institute for Data Science.

Package for analyzing calibrated (level 1) EUV imaging data from AIA.

Solar and heliospheric image visualization tool.

Solar and heliospheric image visualization tool including image parameters with Extended Spatiotemporal Querying Capabilities.

Python library that allows for efficient browsing through IRIS satellite data in order to simplify machine learning applications.

Tutorial 6: Data Sources, Tools, and Techniques for Big Data-driven Machine Learning in Heliophysics

Recent science topics targeting SDO data

machine learning toolbox and developer community building open science AI applications for space science and exploration.

Massive image parameter dataset extracted from the Solar Dynamics Observatory (SDO) mission's AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels.

Dataset curated by experts containing three solar event categories (coronal holes, sunspots, and prominences). Suitable for object detection using deep learning-based models.

The CALLISTO spectrometer is a programmable heterodyne receiver used for observation of solar radio bursts for astronomical science, education, outreach and citizen science as well as rfi-monitoring.

Dataset with selected representative observations for different observation classes.

Data products from the Solar Dynamics Observatory, as well as certain other missions and instruments, are available from the JSOC database.

Useful search interface by Lockheed Martin with links to various resources for each observation. Use our

Xu, Long, Yihua Yan, and Xin Huang. "Deep Learning in Solar Astronomy."

Bobra, Monica, and James Paul Mason. "Machine learning, statistics, and data mining for heliophysics."

Camporeale, Enrico, Simon Wing, and Jay Johnson, eds. "Machine learning techniques for space weather." Elsevier, 2018.