Which machine learning innovation, originally developed for retail customer behavior prediction, was adapted by NASA in 2020 to improve how the James Webb Space Telescope identifies exoplanets?

The astronomy industry has embraced machine learning to revolutionize how we process and analyze vast amounts of telescope data. Modern space observatories generate petabytes of data that would be impossible to analyze manually. Machine learning algorithms now help astronomers detect faint galaxies, classify celestial objects, and even predict cosmic events. Test your knowledge about this fascinating intersection of retail machine learning principles applied to astronomical research!

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TransitNet: A neural network that identifies minute light fluctuations when planets cross in front of stars, adapted from retail inventory prediction systems
AstroBasket: An algorithm originally designed for shopping basket analysis that was repurposed to analyze spectroscopic data from distant stars
StellarMatch: A recommendation engine similar to Amazon's that was modified to match stellar observations with theoretical models
CosmicSegment: A customer segmentation tool repurposed to classify galaxies based on their morphological characteristics