IRIS BIG DATA


Automatic Analysis of Solar Eruptions
in Data from the NASA space telescope IRIS
using machine learning methods

ROLE OF THE INSTITUTE FOR DATA SCIENCE


> Computer science: software architecture, machine learning, algorithms

> Solar Physics: Physics on the sun, characteristics of the data collected by NASA’s IRIS instrument

Project lead at i4Ds: Martin Melchior, Säm Krucker

Partners: UniGE

Funding: National Research Programme NRP 75 „Big Data”
NRP75 project description

Start: 2017
Status: ongoing


Keywords: data science, big data, machine learning, space weather, heliophysics, IRIS

SUMMARY

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.

PEOPLE @I4DS WHO WORK ON IRIS BIG DATA

Dr. Cédric Huwyler

Data Scientist, Astrophysicist

Brandon Panos

Doctoral Student

Dr. Lucia Kleint

Astrophysicist

Prof. Dr. Säm Krucker

Astrophysicist

more information

Prof. Dr. Martin Melchior

Physicist, Deputy Head I4DS

more information

OPEN RESOURCES AND RESULTS

VISUALS AND AUDIO

IRIS is NASA’s newest solar satellite. It produces about 15 TB of data. Scientists use them to determine the thermodynamical properties on the Sun.

IRIS records the Sun in unprecedented details, both as images and as spectra. The AASE project aims at developing an algorithm that automatically detects solar flares among the huge amount of data.