Planetary securityData Science Challenge Tackles Asteroid Detection

Published 30 June 2021

Asteroids could pose an existential threat to humanity, so a timely identification of a menacing Earth-bound asteroid is a life-or-death issue. The Data Science Challenge allow students to compete in applying deep learning models to optical astronomy data to detect and identify Near Earth Objects.

Over three weeks, students from the University of California, Merced collaborated online with mentors at Lawrence Livermore National Laboratory (LLNL) to tackle a real-world challenge problem: using machine learning to identify potentially hazardous

The Data Science Challenge was the third such annual event for LLNL and UC Merced and the second held in a virtual format. Meeting three times per week over Webex, 22 UC Merced students engaged with LLNL scientists on exercises and assignments, attended seminars, took virtual tours and worked on deep learning models with their peers. UC Merced graduate students served as leads for four teams of students and provided skill development on “off-days,” where they discussed data science fundamentals and exposed them to data visualization, neural networks and projects outside the Lab.

Throughout the event, the teams tackled problems around the theme of “Astronomy for Planetary Defense.” For the main challenge, students were tasked with applying deep learning models to optical astronomy data to detect and identify Near Earth Objects (such as asteroids).

The students began with an image classification tutorial before moving on to building classifier models to identify stars and galaxies, and finally asteroids, using image data from the Zwicky Transient Facility (ZTF), an astronomical survey at the Palomar Observatory near San Diego. LLNL is an institutional member of ZTF under a partnership between LLNL’s Data Science Institute (DSI) and the Space Science and Security Program within the Lab’s Global Security Directorate.

LLNL computer scientist Brian Gallagher, who took over as Data Science Challenge director after serving as a mentor last year, said leading the program during COVID-19 was a “trial by fire,” but the event achieved its goals thanks to the combined effort of numerous co-organizers including LLNL administrative specialist Jennifer Bellig and UC Merced applied math professor Suzanne Sindi, as well as Lab mentors and student team leads.