Faint exoplanets detected using computational approach to remove glare from stars

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

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 c…

Astronomers sharpen JWST’s view of distant worlds

Model of the telescope’s imperfections helps strip away starlight obscuring faint exoplanets

Faint exoplanets detected using computational approach to remove glare from stars

An image-processing framework for detecting exoplanets directly has been developed. The technique overcomes the telescope-stability barrier that has limited previous methods, reveals fainter exoplanets than other approaches can detect and could make imaging exo-Earths easier for future observatories. A physics-based model enables the observation of exoplanets that were undetectable using standard techniques.

Hidden planet emerges from the glare

Stars are so bright that their glare overwhelms faint nearby planets. Scientists combined detailed physics from telescope observations with modern computational techniques to precisely remove this glare.

New algorithm strips starlight from JWST images to reveal a hidden giant

A new physics-based computational method called FLIP improved JWST's sensitivity for detecting faint planets by up to 15 times and recovered the giant exoplanet 51 Eridani b from existing images where conventional processing could not see it.