IRIS Big Data
Machine learning methodology to detect, analyse and possibly predict solar flares from data provided by NASA's space telescope IRIS.
Interface Region Imaging Spectrograph (IRIS) is a NASA Small Explorer Mission designed to observe the transition region between the solar chromosphere and corona. It records around 12 GB of image data every day, amounting to a current total of >35 TB of available data.
Current telescopes deliver huge amounts of data which cannot be handled by traditional methods anymore. This project uses machine learning to detect and analyse solar flares in data from the NASA space telescope IRIS. The new methods are expected to significantly contribute to the understanding of the physics behind solar flares. They will also improve capabilities to predict them, a core element in space weather prediction.
Execution | |
Duration | since 2017 |
Funding | |
Project Lead |