NASA and Google using AI to hunt down potentially habitable planets

Astrobiologists are mostly interested in rocky exoplanets that lie in the habitable zone around their parent stars, where liquid water may exist on its surface. NASA’s Kepler spacecraft has spotted a handful of these in the so-called Goldilocks Zone – where it’s not too cold or too hot for life.

As such, a second team from Google and NASA’s lab has built a machine-learning-based tool known as INARA that can identify the chemical compounds in a rocky exoplanet’s atmosphere by studying its high-resolution telescope images.

To develop this software, the brainiacs simulated more than three million planets’ spectral signatures – fingerprints of their atmospheres’ chemical makeups – and labelled them as such to train a convolutional neural network (CNN). The CNN can therefore be used to automatically estimate the chemical composition of a planet from images and light curves of its atmosphere taken from NASA’s Kepler spacecraft. Basically, a neural network was trained to link telescope images to chemical compositions, and thus, you should it a given set of images, and it will spit out the associated chemical components – which can be used to assess whether those would lead to life bursting on the scene.

INARA takes seconds to figure out the biological compounds potentially present in a world’s atmosphere. “Given the scale of the datasets produced by the Kepler telescopes, and the even greater volume of data that will return to Earth from the soon-to-be-launched Transiting Exoplanet Survey Satellite (TESS) satellite, minimizing analysis time per planet can accelerate this research and ensure we don’t miss any viable candidates,” Mascaro concluded. ®

Source: Finally, a use for AI and good old-fashioned simulations: Hunting down E.T. in outer space • The Register

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