Characterising the heating and cooling imprints of the solar corona

We live within the Sun's atmosphere - the solar corona - a highly energetic and multi-million-degree environment permeated by solar magnetic fields. The mechanisms behind the high energy of the corona constitute one of the major open questions in astrophysics. The potential key to the mysterious heating mechanism has been recently directly observed by high-resolution observations, making major worldwide headlines (Antolin et al., 2021, Nature Astronomy). The imprint of the heating was found to be a "nanojet": ubiquitous tiny jet-like structures with specific morphology and dynamics. The next question in the path to solving the coronal heating puzzle is how prevalent nanojets are on the Sun?

The solar corona also conceals a cooling problem. Like snowflakes in the oven, a hundred times colder and denser clumps of gas mysteriously form in the solar corona and fall in a spectacular phenomenon termed coronal rain. This cooling phenomenon is linked to the coronal heating mechanism, and a central open question is how much coronal rain permeates the corona?

We can now tackle these two essential questions of our Sun's corona with a machine learning approach through the development of neural networks. You will learn how to programme in Python and develop cutting-edge deep learning software to automatically detect nanojets and coronal rain observed with NASA/ESA satellites. In turn, you will have the opportunity to apply the methods developed to real-life problems by undertaking a placement with our partners.