A new AI-powered tool has reduced astronomers' workload by 85% by filtering through thousands of data alerts to identify the few genuine signals caused by supernovae (powerful explosions from dying stars). The findings have been published in The Astrophysical Journal.
Finding the needles in a cosmic haystack
Supernovae are rare, bright explosions marking the death of massive stars, events that help scientists understand the origin of chemical elements. These explosions appear unexpectedly across the night sky and must be spotted quickly before they fade, essentially a cosmic game of spot the difference.
A team of researchers led by Oxford University and Queen's University Belfast is searching for these using the Asteroid Terrestrial Impact Last Alert System (ATLAS). This system, originally built as an asteroid impact early warning system, scans the entire visible sky every 24 to 48 hours using five telescopes located around the globe. It is a NASA-funded project, led by the University of Hawaii, and Oxford processes the data for high explosions beyond our galaxy.
The search yields millions of potential alerts nightly, most of which are noise, either instrumental errors or known objects.
Even after applying standard filtering and automated image analysis techniques, the researchers were left with between 200 and 400 candidate signals each day that needed to be manually sifted through. Only a handful of these would be genuinely interesting phenomena, such as supernovae or extragalactic transients (the optical counterparts to gamma ray bursts).
"This manual verification would take several hours each day," added Dr Stevance. "Thanks to our new tool, we can free up scientists' time for what they do best: creative problem solving and questioning the nature of our Universe. It's the astrophysical equivalent of having a bot doing your laundry so you can focus on your art!"
Dr Heloise Stevance and Professor Stephen Smartt with the Asteroid Terrestrial Impact Last Alert System (ATLAS), in the Astrophysics Data Lab, University of Oxford. Credit: Caroline Wood. Credit: Caroline Wood.
The Virtual Research Assistant
The new tool, called the Virtual Research Assistant (VRA), is a collection of automated bots that mimics the human decision-making process by ranking alerts based on their likelihood of being real, extragalactic explosions.
Unlike many AI-automated approaches that require vast amounts of training data and supercomputers, the VRA employs a leaner approach. Instead of data-hungry deep learning methods, the system employs smaller algorithms based on decision trees that search for patterns in specific aspects of the data. This allows scientists to inject their expertise directly into the model and guide the algorithms to key features to look for.
Crucially, the VRA updates its assessment each time a telescope revisits the same patch of sky. This means that a signal is automatically re-checked and re-scored over several nights, with only the most promising candidates being passed on to human astronomers for review.
In its first year of use, the VRA successfully filtered over 30,000 alerts while missing fewer than 0.08% of real supernovae alerts. This ultimately reduced the number of alerts passed on to human 'eyeballers' for verification by around 85%, while retaining more than 99.9% of genuine supernovae candidates.
Since December 2024, the VRA has been linked with the South African Lesedi Telescope, allowing it to automatically trigger follow-up observations for the most promising signals, even before a human has reviewed the data. This has already led to the confirmation of new supernovae.
Study co-author Professor Stephen Smartt (Department of Physics, University of Oxford) said: "The speed and accuracy of this tool will supercharge our team's ability to find and study strange and rare phenomena in the cosmos, for instance, explosions from dying stars in distant galaxies, that can teach us how the chemical elements are created and how fast the Universe is expanding. We will also be able to more efficiently match optical sources to emissions in the gamma ray, x-ray and radio frequencies and possibly gravitational waves. The speed and accuracy of the models are impressive."
The future is bright
This achievement comes just in time, with the upcoming launch of the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) in early 2026. Over ten years, this will survey the entire southern hemisphere sky every few days, ultimately generating over 500 petabytes of images and data.
"The LSST is set to deliver over 10 million alerts each night, finding everything from moving asteroids, supernovae, matter falling onto black holes, merging neutron stars and probably new phenomena as well. Our job as astronomers will be to keep up with this avalanche of data," added Dr Stevance. "Tools like our new Virtual Research Assistant will be invaluable in helping us to better understand how supernovae and their massive stars made all the chemical elements necessary for the world as we know it—from hydrogen to apple pies."
Dr Stevance is currently building Virtual Research Assistants for the UK and European LSST data brokers (Lasair, Fink). Her ambition is to utilize the LSST data to develop bots that can pre-emptively search for supernovae by predicting when and where they will explode.
Dr Stevance added: "In astronomy new knowledge is extracted from data, and LSST will be revolutionary: in its first year alone, it will capture more data than every survey ever. I feel so privileged to live and work at such a historical moment."
*A petabyte is equivalent to 10^15 bytes or one million gigabytes (GB).
Dr Stevance's position is funded by The Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship scheme.
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