AI Will Provide Much Needed Shortcut In Finding Earthlike Exoplanets
In the search for earthlike planets, AI is playing more and more of a role. But first one must define what is meant by earthlike. That's not an easy definition and is the cause of much confusion in the mainstream media.
When planetary scientists say that a planet is earthlike, they really mean it's an earth mass planet that lies in the so-called habitable zone of any given extrasolar planetary system. That's loosely defined as the zone in which a given planet can harbor liquid water at its surface. But there's no guarantee that it has oceans, beaches, fauna, flora, or anything approaching life.
Yet Jeanne Davoult, a French astrophysicist at the German Aerospace Center (DLR) in Berlin, is at the vanguard of using artificial intelligence to speed up the process of finding earthlike planets using AI modeling and algorithms that would boggle the minds of mere mortals.
In a recent paper, appearing in the journal Astronomy & Astrophysics, Davoult, the paper's lead author writes that the aim is to use AI to predict which stars are most likely to host an earthlike planet. The goal is use AI to avoid blind searches, minimize detection times, and thus maximize the number of detections, she and colleagues at the University of Bern write.
Using a previous study on correlations between the presence of an earthlike planet and the properties of its system, we trained an AI Random Forest, a machine learning algorithm, to recognize and classify systems as 'hosting an earthlike planet' or 'not hosting an earthlike planet,' the authors write.
For planetary detection, we try to identify patterns in data sets, and patterns which correspond to planets, Davoult tells me via telephone.
Understanding and anticipating where earthlike planets form first, and thus targeting observations to avoid blind searches, minimizes the average observation time for detecting an earthlike planets and maximizes the number of detections, the authors write.
But among the estimated 6000 exoplanets thus detected in the last 30 years, only some 20 systems with at least one earthlike planet have been found, says Davoult.
In fact, stars smaller than the Sun --- such as K-spectral type dwarfs as well as the ubiquitous red dwarf M-spectral type stars which make up most of the stars in the cosmos, all have longer lifetimes than our own G-spectral type star.
Thus, because of their long stellar lifetimes, it's probably more likely for intelligent life to develop around these K and M types of stars, says Davoult. We are also focusing a lot on M dwarfs because it's easier to detect an earthlike planet around the stars than around sun like stars, because the habitable zone is closer to the stars, so the orbital period is shorter, she says.
The three populations of synthetic systems used in this study differ only in the mass of the central star, the authors write. This single difference directly influences the mass of the protoplanetary disk and thus the amount of material available for planet formation, note the authors. As a result, the three populations exhibit different occurrences and properties for the same type of planet, highlighting the importance of studying various types of stars, they write.
We have developed a model using a Random Forest Classifier to predict which known planetary systems are most likely to host an earthlike planet, the authors write.
It's hard to really compare synthetic planetary populations and real planetary populations, because we know that our model is not perfect, says Davoult. But if you just take the big pattern at the system level, then I'm convinced it's a very powerful tool, she says.
If we observe a planet within a given solar system, it doesn't mean that we've detected all the planets in this planetary system, says Davoult. That's because an earthlike planet might be a bit too far away from the star, or too small to detect, she says. In contrast, my model takes what we already know about planetary system and tells us if there is a possibility for an undetected earthlike planet to exist in the same planetary system, says Davoult.
Davoult is specifically looking for terrestrial planets in the habitable zone of their parent stars.
The very first step is just to detect them and create a database of earthlike planets, even if we have no clue about the composition of their atmospheres, says Davoult.
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