AI restores clarity to James Webb Space Telescope images.

Researchers at the University of Sydney used an AI system called AMIGO to correct detector distortions and restore the James Webb Space Telescope's interferometric infrared images.

Two researchers at the University of Sydney used artificial intelligence to correct subtle detector distortions in the James Webb Space Telescope's infrared interferometry instrument. The system, named AMIGO, applies simulations and neural networks to model the telescope's optics and electronics and to digitally 'deblur' data. The issue involved a faint electronic effect in the detector, where electric charge spreads to neighbouring pixels, known as the brighter-fatter effect. The software-based calibration restored the Aperture Masking Interferometer's imaging precision without a physical repair mission. Key facts: - AMIGO stands for Aperture Masking Interferometry Generative Observations and is the AI-driven calibration system described by the researchers. - The correction targets a detector distortion called the brighter-fatter effect that caused subtle image blurring in the JWST's Aperture Masking Interferometer (AMI). - The technique uses simulations and neural networks to replicate instrument behaviour and to reverse the distortion from Earth. - With the improved calibration, JWST produced clearer images, including a direct image of a faint exoplanet and a red-brown dwarf near HD 206893, a black hole jet, the surface of Jupiter's moon Io, and dust-filled winds of WR 137. - The AMI was the JWST component designed in Australia by Professor Peter Tuthill and colleagues at the University of Sydney. Summary: The AI calibration restored AMI's intended resolving power, allowing JWST to capture fainter and sharper targets across several examples of astronomical interest. Undetermined at this time.