Machine Learning Will be One of the Best Ways to Identify Habitable Exoplanets
upstart writes:
Machine learning will be one of the best ways to identify habitable exoplanets:
The field of extrasolar planet studies is undergoing a seismic shift. To date, 4,940 exoplanets have been confirmed in 3,711 planetary systems, with another 8,709 candidates awaiting confirmation. With so many planets available for study and improvements in telescope sensitivity and data analysis, the focus is transitioning from discovery to characterization. Instead of simply looking for more planets, astrobiologists will examine "potentially-habitable" worlds for potential "biosignatures."
[...] Water is something that all life on Earth depends on, hence its importance for exoplanet and astrobiological surveys. As Lisa Kaltenegger told Universe Today via email, this importance is reflected in NASA's slogan-"just follow the water"-which also inspired the title of their paper.
"Liquid water on a planet's surface is one of the smoking guns for potential life-I say potential here because we don't know what else we need to get life started. But liquid water is a great start. So we used NASA's slogan of 'just follow the water' and asked, how can we find water on the surface of rocky exoplanets in the habitable zone? Doing spectroscopy is time intensive, thus we are searching for a faster way to initially identify promising planets-those with liquid water on them."
Currently, astronomers have been limited to looking for Lyman-alpha line absorption, which indicates the presence of hydrogen gas in an exoplanet's atmosphere. This is a byproduct of atmospheric water vapor that's been exposed to solar ultraviolet radiation, causing it to become chemically disassociated into hydrogen and molecular oxygen (O2)-the former of which is lost to space while the latter is retained.
This is about to change, thanks to next-generation telescopes like the James Webb (JWST) and Nancy Grace Roman Space Telescopes (RST), as well as next-next-generation observatories like the Origins Space Telescope, the Habitable Exoplanet Observatory (HabEx), and the Large UV/Optical/IR Surveyor (LUVOIR). There are also ground-based telescopes like the Extremely Large Telescope (ELT), the Giant Magellan Telescope (GMT), and the Thirty Meter Telescope (TMT).
Journal Reference:
Pham, Dang, Kaltenegger, Lisa. Follow the Water: Finding Water, Snow and Clouds on Terrestrial Exoplanets with Photometry and Machine Learning, (DOI: 10.48550/arXiv.2203.04201)
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