Fast-Tracking the Search for Habitable Worlds

Astronomers have detected thousands of planets, including dozens that are potentially habitable. To winnow them down, they need to understand their atmospheres and other factors. (NASA Illustration)
Astronomers have detected thousands of planets, including dozens that are potentially habitable. To winnow them down, they need to understand their atmospheres and other factors. (NASA Illustration)

Modern astronomy would struggle without AI and machine learning (ML), which have become indispensable tools. They alone have the capability to manage and work with the vast amounts of data that modern telescopes generate. ML can sift through large datasets, seeking specified patterns that would take humans far longer to find.

The search for biosignatures on Earth-like exoplanets is a critical part of contemporary astronomy, and ML can play a big role in it.

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Finding Atmospheres on Red Dwarf Planets Will Take Hundreds of Hours of Webb Time

This illustration shows what exoplanet K2-18 b could look like based on science data. NASA’s James Webb Space Telescope examined the exoplanet and revealed the presence of carbon-bearing molecules. The abundance of methane and carbon dioxide, and shortage of ammonia, support the hypothesis that there may be a water ocean underneath a hydrogen-rich atmosphere in K2-18 b. But more extensive observations with the JWST are needed to understand its atmosphere with greater confidence. Image Credit: By Illustration: NASA, ESA, CSA, Joseph Olmsted (STScI)Science: Nikku Madhusudhan (IoA)

The JWST is enormously powerful. One of the reasons it was launched is to examine exoplanet atmospheres to determine their chemistry, something only a powerful telescope can do. But even the JWST needs time to wield that power effectively, especially when it comes to one of exoplanet science’s most important targets: rocky worlds orbiting red dwarfs.

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