Now, astronomers have developed a new way to spot planets that were formerly drowned out in a sea of light. Using a combination of modeling and machine learning techniques, they picked out a previously hidden giant exoplanet in data from JWST. The results were published Nov. 4 in the journal Nature.
Telescopes like JWST use instruments called coronagraphs to block out some of this excess light, similar to the way the moon and a pinhole camera operate during a total solar eclipse. But even the best coronagraphs have limitations — for example, they can't block all the glare, and the telescopes themselves move.
To address this, Ferrer-Chavez and his team developed a "digital twin" of JWST that virtually recreates how light travels through the telescope's detectors, minus any movement-based imperfections. To fine-tune this digital replica, the researchers used computational models based in real-world physics alongside machine learning techniques similar to those used to train AI. But the authors stressed that this is not an AI model.
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Using FLIP, the team analyzed data from three stars, including 51 Eridani, which is located around 97 light-years from Earth and was already suspected to have a giant exoplanet orbiting it. Although the planet, called 51 Eridani b, had been detected by ground-based telescopes, it was drowned out by light in previous JWST observations. With FLIP, however, 51 Eridani b popped out from the dataset.
"It's not that [solar-system-like planets] don't exist," Ferrer-Chavez said. "It's that we can't see them."
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