https://www.sciencedaily.com/releases/2022/07/220706133309.htm

As space instrument packages collect increasingly complex data in ever-increasing volumes, it is becoming more challenging for scientists to process and analyze relevant trends. Machine learning (ML) is becoming a critical tool for processing large complex datasets, where algorithms learn from existing data to make decisions or predictions that can factor more information simultaneously than humans can. However, to take advantage of ML techniques, humans need to label all the data first -- often a monumental endeavor.

"Labeling data with meaningful annotations is a crucial step of supervised ML. However, labeling datasets is tedious and time consuming," said Dr. Subhamoy Chatterjee, a postdoctoral researcher at SwRI specializing in solar astronomy and instrumentation and lead author of a paper about these findings published in the journal Nature Astronomy. "New research shows how convolutional neural networks (CNNs), trained on crudely labeled astronomical videos, can be leveraged to improve the quality and breadth of data labeling and reduce the need for human intervention."