Boffins use machine-learning tricks to create a starlight dimmer switch

FLIP reveals a planet hidden in Webb data, boosting detection sensitivity by up to 15 times

Scientists have developed a way to remove starlight from telescope images that improved sensitivity to faint exoplanets by up to 15 times in tests on James Webb Space Telescope data.

A team led by Northwestern University's Rodrigo Ferrer-Chavez combined a physics-based telescope model with optimization techniques used in machine learning to subtract unwanted starlight.

Directly imaging exoplanets remains difficult because their host stars' glare can overwhelm their faint light. Existing techniques subtract modeled or reference starlight, but tiny changes in telescope optics can leave residual glare that hides planets.

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The team's Physics-based Learning for Imaging Planets (FLIP) framework models how light passes through the telescope, adjusts for optical imperfections, and simulates the glare to be subtracted.

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Applied to observations from Webb's Near-Infrared Camera (NIRCam), FLIP produced the first image of the known giant planet 51 Eridani b at an infrared wavelength of 4.3 micrometers, where conventional processing had failed. Tests showed sensitivity improvements of two to 15 times over conventional techniques, according to the Nature paper.

"This is the first time, to our knowledge, that such an approach has demonstrated substantially improved sensitivity on real, on-sky coronagraphic imaging. Our framework can be extended to other current and future ground-based and space-based observatories… potentially relaxing the unprecedented stability requirements needed to make these a success," the paper says.

Further work includes reducing computational demands and developing ways to handle the much larger data volumes produced by ground-based observations.

The researchers hope further development and testing will bring direct images of Earth-like planets closer, while reducing the extent to which tiny telescope instabilities obscure them. ®