The ATLAS Virtual Research Assistant

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• Heloise Stevance, University of Oxford
• Monday 10 February 2025, 12:00-12:30
• FW11, Willam Gates building (Department of Computer Science and Technology).

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The ATLAS sky survey is able to image the whole night sky every 24 to 48 hours, looking for near earth asteroids and exploding stars. This generates 10s of millions of potential alerts every day which must be triaged and filtered to find the few explosions worth following up with more (expensive and time consuming) resources. Much of the work can be automated, but human “eyeballing” remains the final step before reporting and follow-up. This is a task that involves crappy images, sparse and uneven time series (with error bars and non-detections), and a whole lot of contextual knowledge to make sense of the mess. We are looking for rare events (not many training samples), we want (near) 100% recall, and explainability is paramount (since faults in the VRA can impact astrophysical event rates). That is a lot to ask of any model. In this presentation I will briefly present how ML is used in the VRA to lower eyeballer workload (decreased by 70%) and why “fancier” ML methods reported in the literature did not address our problems. I will also touch on upcoming sky survey challenges.

This talk is part of the Accelerate Lunchtime Seminar Series series.

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