Many of our imagined sci-fi futures pit humans and machines against each other — but what if they collaborated instead? This may, in fact, be the future of astronomy.
As data sets grow larger and larger, they become more difficult for small teams of researchers to analyze. Scientists often turn to complex machine-learning algorithms, but these can’t yet replace human intuition and our brains’ superb pattern-recognition skills. However, a combination of the two could be a perfect team. Astronomers recently tested a machine-learning algorithm that used information from citizen-scientist volunteers to identify exoplanets in data from NASA’s Transiting Exoplanet Survey Satellite (TESS).
“This work shows the benefits of using machine learning with humans in the loop,” Shreshth Malik, a physicist at the University of Oxford in the U.K. and lead author of the publication, told Space.com.
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The researchers used a typical machine-learning algorithm known as a convolutional neural network. This computer algorithm looks at images or other information that humans have labeled correctly (a.k.a “training data”), and learns how to identify important features. After it’s been trained, the algorithm can identify these features in new data it hasn’t seen before.
For the algorithm to perform accurately, though, it needs a lot of this labeled training data. “It’s difficult to get labels on this scale without the help of citizen scientists,” Nora Eisner, an astronomer at the Flatiron Institute in New York City and co-author on the study, told Space.com.
People from across the world contributed by searching for and labeling exoplanet transits through the Planet Hunters TESS project on Zooniverse, an online platform for crowd-sourced science. Citizen science has the extra benefit of “sharing the euphoria of discovery with non-scientists, promoting science literacy and public trust in scientific research,” Jon Zink, an astronomer at Caltech not affiliated with this new study, told Space.com.
Finding exoplanets is tricky work — they’re tiny and faint compared to the massive stars they orbit. In data from telescopes like TESS, astronomers can spot faint dips in a star’s light as a planet passes between it and the observatory, known as the transit method.
However, satellites jiggle around in space and stars aren’t perfect light bulbs, making transits sometimes tricky to detect. Zink thinks partnerships with machine learning “could significantly improve our ability to detect exoplanets” in this kind of real-world, noisy data.
Some planets are harder to find than others, too. Long-period planets orbit their star less frequently, meaning a longer period of time between dips in the light. TESS only studies each patch of sky for a month at a time, so for these planets may only capture one transit instead of several periodic changes.
“With citizen science, we are particularly good at identifying long-period planets, which are the planets that tend to be missed by automated transit searches,” Eisner said.
This work has the potential to go far beyond exoplanets, as machine learning is quickly becoming a popular technique across many aspects of astronomy, Malik said. “I can only see its impact increasing as our datasets and methods become better.”
The research was presented at the Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS) in December and is described in a paper posted to the preprint server arXiv.org.
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