Astronomers also hope that in the near future, machine learning will help them identify which planets might be habitable. Using next-generation observatories like the Nancy Grace Roman Telescope and James Webb Space Telescope (JWST), astronomers intend to use ML to detect water, ice, and snow on rocky planets.
Galactic forgeries
While many ML models are trained to distinguish between different types of data, others are intended to produce new data. These generative models are a subset of AI techniques that create artificial data products, such as images, based on some underlying understanding of the data used to train it.
The series of DALL-E models developed by the research company OpenAI — and the free-to-use imitator it inspired, DALL-E mini — have pushed this concept into the public eye. These models generate an image from any written prompt and have set the internet alight with their uncanny ability to construct images of, for instance, Garfield inserted into episodes of Seinfeld.
You might think that astronomers would be wary of any kind of fake imagery, but in recent years, researchers have turned to generative models in order to create galactic forgeries. A paper published Jan. 28 in Monthly Notices of the Royal Astronomical Society describes using the method to produce incredibly detailed images of fake galaxies, which can be used to test predictions from enormous simulations of the universe. They can also help develop and refine the data processing pipelines for next-generation surveys.
Some of these algorithms are so good that even professional astronomers can struggle to distinguish between the real and the fake. Take this recent entry into NASA’s Astronomy Picture of the Day webpage, which features dozens of synthetically generated images of objects in the night sky — and just one real image.
Searching for serendipity
AI is also primed to make discoveries that we cannot predict. There’s a long history of discoveries in astronomy that happened because someone was in the right place, at the right time. Uranus was discovered by chance when William Herschel was scanning the night sky for faint stars, Vesto Slipher measured the speed of spiral arms in what he thought were protoplanetary disks — eventually leading to the discovery of the expanding universe — and Jocelyn Bell Burnell’s famous detection of pulsars happened while she was analyzing measurements of quasars.
Perhaps soon, an AI could join these ranks of serendipitous discoverers though a field of techniques called anomaly detection. These algorithms are specifically trained to sift through mountains of images, light curves, and spectra, looking for the samples that don’t look like anything we’ve seen before. In the next generation of astronomy, with its petabytes of raw data from observatories like the Rubin and JWST, we can’t possibly imagine what these algorithms might find.