Kyle Boon Wins Machine-Learning Competition

Friday, January 25, 2019

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 platform, provided a simulated data set for 3 million objects and tasked participants with identifying which of 15 classifications was the best fit for each object.

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.” Boone said. Participants could submit their codes up to five times per day to check their performance on a leaderboard for 1 million objects in the test set. The competition ran from Sept. 28, 2018, to Dec. 17, 2018, and Boone was up against 1,383 other competitors on 1,093 teams.

“During the last few weeks I worked really hard on it,” he said, devoting all of his evenings and weekends to intense coding.

“My results started to become competitive, and I rushed to implement all of the different ideas that I was coming up with. It was fun, and several teams were neck and neck until the end. I learned a lot about how to tune machine-learning algorithms. There are a lot of little ‘knobs’ you can tweak to get that extra 1 percent performance.”

While giving a science talk on the final day of the competition, he received a text from his fiancée. “She messaged me and said, ‘Congratulations.’ That was pretty exciting,” Boone said. He won $12,000 for his first-place finish, and also participated in a second phase of the competition that was more open-ended and is driving toward more applicable solutions in categorizing the objects that LSST will see – the latest round concluded Jan. 15.

Editor: 
Glenn Roberts Jr.