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Habitable Exoplanets to Be Studied with Help of AI and Photonics

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Researchers from the University of Sydney developed a sensor capable of correcting the distortion of starlight caused by heat variations in Earth’s atmosphere. The sensor would allow ground-based telescopes to study habitable exoplanets.

The technology builds on a method to measure and correct the wavefront of light as it passes through atmospheric turbulence directly at the focal plane of an imaging instrument. This is done using a photonic lantern, to convert light, that is linked to a neural network inference process. The researchers called the device a photonic wavefront sensor, which can be placed directly in the optical instrument where the image forms.
In the photonics lab at the Sydney Nanoscience Hub, University of Sydney. (From left to right): Alison Wong, Christopher Betters, Barnaby Norris, Sergio Leon-Saval. Courtesy of the University of Sydney.
In the photonics lab at the Sydney Nanoscience Hub, University of Sydney, from left: Alison Wong, Christopher Betters, Barnaby Norris, Sergio Leon-Saval. Courtesy of the University of Sydney.

The device can measure and correct the distortion caused by the atmosphere using a telescope’s adaptive optics system thousands of times per second to produce a clear image.

“This new sensor merges advanced photonic devices with deep learning and neural networks techniques to achieve an unprecedented type of wavefront sensor for large telescopes,” said Barnaby Norris of the University of Sydney.

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The technology will allow ground-based telescopes to image exoplanets orbiting distant stars from Earth. Over the past two decades, thousands of exoplanets have been detected, though seldom by ground-based instruments.

“It is really hard to separate a star’s ‘twinkle’ from the light dips caused by planets when observing from Earth,” Norris said. “Most observations of exoplanets have come from orbiting telescopes, such as NASA’s Kepler. With our invention we hope to launch a renaissance in exoplanet observation from the ground.”

The sensor will be deployed in the 8.2-m Subaru Telescope in Hawaii, operated by the National Astronomical Observatory of Japan.

“This is no doubt a very innovative approach and very different to all existing methods,” said Oliver Guyon of the University of Arizona. “It could potentially resolve several major limitations of the current technology. We are currently in collaboration with the University of Sydney team toward testing this concept at Subaru in conjunction with SCExAO, which is one of the most advanced adaptive optics systems in the world.”

According to Sergio Leon-Saval, director of the Sydney Astrophotonic Instrumentation Laboratory at the University of Sydney, the technique could prove useful in a wide range of fields including optical communications, remote sensing, and in vivo imaging. These applications each require the reception and/or transmission of accurate wavefronts through a turbulent or turbid medium, such as water, blood, and air.

The research was published in Nature Communications (www.doi.org/10.1038/s41467-020-19117-w).

Published: October 2020
Glossary
atmospheric turbulence
Irregularities and disturbances in the atmosphere that are of particular interest because they induce random temporal and spatial phase and amplitude fluctuations that destroy the optical quality and the coherence properties of laser beams.
deep learning
Deep learning is a subset of machine learning that involves the use of artificial neural networks to model and solve complex problems. The term "deep" in deep learning refers to the use of deep neural networks, which are neural networks with multiple layers (deep architectures). These networks, often called deep neural networks or deep neural architectures, have the ability to automatically learn hierarchical representations of data. Key concepts and components of deep learning include: ...
neural network
A computing paradigm that attempts to process information in a manner similar to that of the brain; it differs from artificial intelligence in that it relies not on pre-programming but on the acquisition and evolution of interconnections between nodes. These computational models have shown extensive usage in applications that involve pattern recognition as well as machine learning as the interconnections between nodes continue to compute updated values from previous inputs.
wavefront
A wavefront refers to the continuous surface or boundary representing points in a wave that are in phase, meaning they have the same phase or position in their respective cycles. In simpler terms, it's the front edge of a wave as it propagates through a medium. For example, in a water wave, the wavefront would be the crest of the wave, representing the points where the water's surface reaches its highest elevation. Similarly, in a sound wave, the wavefront would represent the points of...
telescope
An afocal optical device made up of lenses or mirrors, usually with a magnification greater than unity, that renders distant objects more distinct, by enlarging their images on the retina.
adaptive optics
Adaptive optics (AO) is a technology used to improve the performance of optical systems by reducing the effects of atmospheric distortions. The Earth's atmosphere can cause light passing through it to experience distortions, resulting in image blurring and degradation in various optical applications, such as astronomical observations, laser communications, and imaging systems. Adaptive optics systems actively adjust the optical elements in real-time to compensate for these distortions. Key...
Research & TechnologyAustraliaHawaiiSubaru Telescopephotonic wavefront sensoratmosphereatmospheric turbulenceexoplanetexoplanet imagingexoplanet researchdeep learningneural networkneural networkswavefrontwavefront sensorOpticstelescopeExtremely Large TelescopeSCExAOadaptive opticsUniversity of Sydney

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