A game of chess has 20 possible opening moves. Imagine being asked to start a game with tens of millions of openings instead. That was the task assigned to Adam Friedman, a 2020 summer intern at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. A chess champion in high school, Friedman analyzed his opponent—a deluge of data on the brightness changes of over 70 million stars.
Click here for original story, Researcher uses machine learning to classify stellar objects from TESS data
Source: Phys.org