首页|Findings from University of Sydney Broaden Understanding of Machine Learning (Ma chine-learning Approach for Optimal Selfcalibration and Fringe Tracking In Phot onic Nulling Interferometry)
Findings from University of Sydney Broaden Understanding of Machine Learning (Ma chine-learning Approach for Optimal Selfcalibration and Fringe Tracking In Phot onic Nulling Interferometry)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is the subject of a report. According to news reporting originating from Sydney, Aust ralia, by NewsRx correspondents, research stated, “Photonic technologies have en abled a generation of nulling interferometers, such as the guided light interfer ometric nulling technology instrument, potentially capable of imaging exoplanets and circumstellar structure at extreme contrast ratios by suppressing contamina ting starlight, and paving the way to the characterization of habitable planet a tmospheres. But even with cutting-edge photonic nulling instruments, the achieva ble starlight suppression (null-depth) is only as good as the instrument's wavef ront control and its accuracy is only as good as the instrument's calibration.”
SydneyAustraliaAustralia and New Zea landCyborgsEmerging TechnologiesMachine LearningUniversity of Sydney
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