Scientists have developed a new machine-learning algorithm that can process data being returned by solar missions, decreasing event detection time.
IMAGE: Using Solar and Heliospheric Observatory data, SwRI developed a tool to efficiently label large, complex datasets, such as the magnetogram on the left, to allow a machine learning application to identify potentially hazardous solar events. Solar flares, coronal mass ejections, prominences and sunspots are all driven by complex magnetic activity within the Sun’s interior and at its surface, illustrated by the ultraviolet image on the right. CREDIT: SwRI
One of the biggest problems we have with all these spacecraft is receiving all of the data they send back to Earth. And once you get all that…
Data Scientists must think like an artist when finding a solution when creating a piece of code. ⚪️ Artists enjoy working on interesting problems, even if there is no obvious answer ⚪️ linktr.ee/mlearning 🔵 Follow to join our 28K+ Unique DAILY Readers 🟠
Planetary scientist, podcast host. Communication specialist for SETI Institute and Planetary Science Institute. Buy me a coffee: https://ko-fi.com/planetarypan