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Algorithms for analyzing the images from the James Webb telescope

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Current space missions, including the James Webb Space Telescope (JWST), benefit from many technological breakthroughs. These are closely related to the massive increase in astronomical imagery. Leveraging this data to hold more information requires the development of new tools.

Developed by NASA in partnership with the European Space Agency (ESA) and the Canadian Space Agency (CSA), the James Webb Space Telescope observes the universe in the near and mid-infrared. This allows us to study waves emitted in distant times. In fact, the universe is expanding, shifting light from the visible spectrum into the infrared, which has migrated since the time when the first stars and galaxies are thought to have formed hundreds of millions of years after the Big Bang.

Algorithms allow you to apply different treatments to optimize JWST settings and correct different noise sources in the raw image.

“Infrared makes it possible to ‘see’ these very distant objects. The expansion of space stretches the waves, transforming the light that has traveled for billions of years. Pierre-Olivier Lagage, an astrophysicist at the French Commission for Alternative Energies and Atomic Energy (CEA) and his MIRI (mid-infrared instrument) science leader in France, explains:

One of the four scientific instruments on board JWST, MIRI operates in the mid-infrared and offers excellent sensitivity. It offers four observation modes: imaging, colonography, low-resolution spectroscopy, and medium-resolution integral field spectroscopy. Colonography, comparable to the movement of a person’s hand in front of the sun when facing the sun, consists of obscuring the central part of the star so that objects orbiting near it can be seen. As for spectroscopy, this consists of studying the spectrum of electromagnetic radiation emitted and absorbed by celestial bodies, each chemical element having a specific profile.

“This is the first time we have access to such a complex instrument in space,” emphasizes Pierre-Olivier Lagage.

“All-new design”

JWST constitutes a major technological leap compared to its predecessors, the Hubble Space Telescope and the Spitzer Space Telescope. It introduces several technological breakthroughs, starting with the ability to deploy the primary mirror. At 6.5 m in diameter, it is lighter than Hubble’s 2.4 m mirror and unfolds after launch.

“More importantly, its shape is adapted to the space thanks to the six actuators on the back of each hexagonal segment,” said Pierre-Olivier Lagage. This is he one of JWST’s most innovative aspects. ”

Another innovation cited by astrophysicists is a giant heat shield that could also be deployed in orbit, passively keeping the temperature of the sensor below a certain threshold. “The James Webb Space Telescope is a completely new design, and NASA has already announced that future telescopes will be inspired by what was done for this mission.”

capture the best images

Several types of algorithms are used to improve the JWST “vision” and final image quality. This allows you to apply different treatments to optimize telescope settings and correct various sources of noise in the raw image.

“At the sensor level, for example, we can reduce the dark current. [a residual electric current in the absence of light] Then we do the flat field correction,” explains Pierre-Olivier Lagage. This flat-field correction process allows us to compensate for variations in the response of different pixels in the telescope to the same light source. The goal is to create a cohesive image.

Various astronomical algorithms (related to the positions of stars and other celestial bodies) can also pinpoint the exact location of radioactive sources. Other software can help improve the pointing accuracy and stability of your telescope. “JWST is so stable that this problem does not arise.

The researchers also mention calibration software that optimizes observation settings to ensure you get the best data.

After that, you can use dedicated software depending on your scientific program. They are often developed in an ‘ad-hoc’ way, implementing numerous statistical analysis techniques and generating more or less complex processing chains.

The study of exoplanets in transit, a field of research by Pierre-Olivier Lagage, provides us with an example. The transit method consists of associating periodic declines in the star’s luminosity with exoplanets orbiting the star. “Variations in light intensity measured by the transit method are minimal and are full of systematic noise. Therefore, using specific ‘detrending’ software, we can determine the variability caused by various sources of noise and instrumental drift.” We remove possible trends,” explains the astrophysicist.

AI and “Space Big Data”

This increase in the amount and complexity of astronomical data will require the development of new tools that will make available more detailed images of the universe than ever before. Artificial intelligence and machine learning are beginning to be used, especially to combine images from different sources and to detect and classify phenomena of interest.

Taking JWST as an example, the telescope now carries several imaging and spectrometers covering different regions of the electromagnetic spectrum. This allows us to provide multispectral and hyperspectral (meaning they are captured in a greater or lesser number of spectral bands) imagery of a scene, with each type of imagery providing specific information. Algorithms using neural networks or other similar techniques help combine these images to construct high-quality spatio-spectral images that combine high resolution and spectral detail.

Identifying weak objects from thousands of astronomical images is a very complex and time-consuming task. Again, powerful AI algorithms help astronomers detect and classify phenomena of interest. Computer vision techniques are used for this, for example. This is the mission of Morpheus, a deep learning algorithm that analyzes celestial images pixel by pixel and can detect and classify galaxies based on morphological criteria. Morpheus was first trained from images of thousands of galaxies captured by Hubble, an ambitious program aimed at mapping parts of the universe with JWST’s NIRCam and his MIRI cameras. Used in a range of his COSMOS-Webb programs. This will help researchers “excavate” this vast region of the universe in search of the oldest galaxies.

Thanks to major advances and novel approaches in the field of equipment, only JWST sees what it sees with unprecedented sensitivity. As far as astronomy is concerned, AI, which is still in its infancy, could become a powerful tool to take full advantage of its extraordinary capabilities, helping us better understand the processes of work in space.

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