With current and future solar telescopes, the Sun is observed in unprecedented detail, making it possible to study its activity on a very small scales and to discover fascinating phenomena. As a result, large volumes of data are collected that cannot be reasonably analyzed with conventional methods. In the last decade, machine learning and neural networks have emerged as powerful tools to select and analyze relevant information from these huge collections. By exploiting symmetries and patterns in the data, these new techniques can be optimized to perform various autonomous tasks (such as classifications, regression problems, dimensionality reduction, and many others) faster and better than conventional methods. In this contribution, I will review a selection of successful applications to various problems in solar physics, including data preprocessing, automatic solar feature segmentation, image deconvolution, acceleration of spectropolarimetric inversions, and prediction of explosive phenomena. Finally, I will discuss outstanding issues and provide an outlook for future research.
This Friday colloquium will be hybrid. Attendees can therefore participate either in-person or via Zoom. Please join via Zoom at
https://uio.zoom.us/j/69001043754?pwd=cEJpbVE5ci9PdWNtRld2TDNNcGtKdz09
Meeting ID:690 0104 3754
Passcode: PeiseStua3
Attendees will be muted during the colloquium, but will have the opportunity to ask questions at the end by clicking on the "raise hand” button (or send a request via chat).