Direct images of exoplanets are rare and lack detail. Future observatories could change that, but right now, images of exoplanets don’t tell researchers much. They only show the presence of the planets as spots of light.
But a new study shows that just a few pixels can help us understand an exoplanet’s surface features.
Astronomers can image exoplanets directly, but only under certain circumstances. Normally, a star’s light washes out the much weaker light from all the exoplanets orbiting it. Exceptions are exoplanets that are very large, very far from their star, or very young. Astronomers can image young planets in the infrared because their thermal output is high, while light from massive exoplanets, or exoplanets far from their stars, is not as badly washed out.
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Rather indistinct images of exoplanet AB Aur b were enough for a team of researchers to advance our understanding of planet formation. And since most exoplanets are found by examining transit light curves, any actual images of exoplanets are exciting. If the authors of a new study are correct, even a few pixels of an exoplanet’s surface can advance our understanding, just as transit light curves did.
The new study is called Global Mapping of Surface Composition on an Exo-Earth Using Sparse Modeling and is available online at the prepress site arxiv.org. Lead author is Atsuki Kuwata from the University of Tokyo Faculty of Astronomy.
The study focuses on the future, when direct imaging of exoplanets becomes more practical. At first, these direct images can only represent a few pixels of an exoplanet’s surface. The question is how can we learn as much as possible from just a few pixels? According to this study, more than one might think at first glance.
In their work, the team explains that “the time series of light reflected from exoplanets through future direct imaging can provide spatial information related to the planet’s surface.” They used spase modeling to extract information from direct exoplanet images. Sparse modeling is a machine learning tool that can detect predictive patterns in data, even when the data is sparse or weak.
The researchers applied their sparse modeling to what they call toy soil. They identified surface features useful for studying exoplanets. “By applying our technique to a toy model of the cloudless Earth, we show that our method can derive sparse and continuous surface distributions and also unmixed spectra without
Preliminary knowledge of the planet’s surface,” they write.
They also applied their technique to actual Earth data from DSCOVR/EPIC. DSCOVR is a NOAA Earth observation satellite and EPIC is a polychromatic camera on the DSCOVR satellite. EPIC is a powerful tool that provides detailed measurements of ozone, aerosols, cloud reflectivity, cloud height, vegetation characteristics and estimates of surface UV radiation. The researchers “dumbed down” all this detailed data on Earth’s surface as if they were looking at a distant exoplanet.
Applying their sparse modeling technique to the DSCOVR/EPIC data, they found patterns they identified as oceans and cloud cover. They also found two components that they identified as land. “We also found two components that resemble land distribution. One of the components captures the Sahara, the other roughly corresponds to vegetation, although their spectra are still cloud-polluted.”
Scientists have been working to extract as much information as possible from the sparse data in exoplanet images. One of the methods is called Tikhonov regularization. The image below compares the team’s sparse modeling to the Tikhonov regularization. “We concluded that sparse modeling provides better inferences about surface distribution and unmixed spectra than the method based on Tikhonov regularization,” the authors write.
This study is a refinement of some previous work, and the results are intriguing. One of the obstacles in this type of work is that planets rotate. For the results to be valid, scientists must account for the rotation of the exoplanet with extreme accuracy. But clouds don’t sit still while we capture their portraits from tens or hundreds of light-years away. The study had to make provisions for this. “In addition, we assumed the surface distribution of the final element to be static, but we should also consider the dynamic movement of surfaces, especially in the case of clouds,” the team writes in its conclusion.
This work takes on new meaning as upcoming telescopes will begin directly imaging exoplanets. This is the realm of our powerful new ground-based telescopes like the upcoming European Extremely Large Telescope (E-ELT) and the Giant Magellan Telescope (GMT). These telescopes are remarkably powerful and provide sharper images than space telescopes. Sharpness is necessary to detect and image direct light from exoplanets.
At present, direct images of exoplanets do not contain much detail. They’re still intriguing and, in some ways, scientifically valuable, but they don’t reveal any surface detail.
Artists are another source of images of exoplanets. Experienced illustrators like Martin Kornmesser from ESA arouse our curiosity and enthusiasm with their data-based representations of distant worlds. We would be in a different place if it wasn’t for Kornmesser and others spreading exoplanet excitement among the general public.
In 2015, GMT project lead Patrick McCarthy told Forbes Magazine, “We should [also] relatively easily see how Jupiter- and Saturn-like planets form around stars in the Milky Way’s Orion and Taurus star-forming complexes.”
But these images won’t be crystal clear, and they won’t show all of a planet’s surface detail. Scientists still have to tease out as much detail as possible from these images using machine learning, modelling, simulations and other tools.
That is why studies like this are essential. They prepare us for what comes next.