Machine-learning code sorts through telescope data

A new telescope will take a sequence of hi-res snapshots with the world's largest digital camera, covering the entire visible night sky every few days - and repeating the process for an entire decade. That presents a big data challenge: What's the best way to rapidly and automatically identify and categorize all of the stars, galaxies, and other objects captured in these images? To help solve this problem, the scientific collaboration that is working on this Large Synoptic Survey Telescope project launched a competition among data scientists to train computers on how to best perform this task. The Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), hosted on the Kaggle.com Kyle Boone, a UC Berkeley graduate student who has been working on computer algorithms in support of the Nearby Supernova Factory experiment and Supernova Cosmology Project efforts at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab), devoted some of his spare time to the international machine-learning challenge in late 2018 while also working toward his Ph.D. "As I worked on job applications I started playing around with this competition to teach myself more about machine learning."