Not only has artificial intelligence (A) been utilized heavily in healthcare and finance, science and astronomy is another field that encourages the boom of AI as astronomers are excited to see how machine learning or deep learning and artificial intelligence will enhance surveys. One field that is already benefitting in the search for extrasolar planets where researchers rely on machine-learning algorithms to distinguish between faint signals and background noise. As this field continues to transition from discovery to characterization, the role of machine intelligence is likely to become even more critical.
According to this recent article, a machine-learning algorithm just found 301 additional planets in Kepler data. All 301 of the machine-validated planets were originally detected by the Kepler Science Operations Center pipeline. These planets were also promoted to the status of planet “candidate” by the Kepler Science Office (in other words, not confirmed). However, before the Kepler Kepler archive was examined using ExoMiner, no one was able to verify that these potential signals were exoplanets.
These newly-detected exoplanets and the ExoMiner algorithm were described in a paper that was recently accepted for publication in the Astrophysical Journal. Like all machine-learning techniques, this new deep neural network learns to identify patterns based on the data it has been provided. In the case of ExoMiner, researchers at NASA Ames designed it using various tests and properties that human experts use to confirm the presence of exoplanets. Combined with NASA’s Supercomputer, it uses this knowledge to distinguish between actual exoplanets and various types of “false positives.”
Also indicated in the paper is how ExoMiner is more precise in ruling out false positives and identifying signatures of planets while also showing science teams how it arrived at its conclusion.
Unfortunately, none of the newly confirmed planets are believed to be “Earth-like,” meaning they are not rocky in composition nor do they orbit within their parent stars’ habitable zone (HZ). But they have some characteristics in common with the overall population of confirmed exoplanets in our galactic neighborhood, making these 301 planets a fitting addition to the exoplanet census.
In the near future, ExoMiner and other machine learning techniques will prove very useful to missions relying on Transit Photometry. This includes TESS, which is scheduled to remain in operation until Sept. 2022.
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