Just a few pixels will allow astronomers to map the oceans and deserts of exoplanets

(ORDO NEWS) — Direct images of exoplanets are rare and lack detail. Future observatories could change that, but for now, exoplanet images don’t tell researchers much. They simply show the presence of the planets as patches of light.

However, a new study shows that just a few pixels can help us understand the surface features of an exoplanet.

Astronomers can directly image exoplanets, but only under certain circumstances. Normally, light from a star washes out much weaker light from exoplanets orbiting it.

The 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 emission is high, while the light from massive exoplanets or exoplanets far from their star is not washed out as much.

Fuzzy images of the exoplanet AB Aur b were enough for one team of researchers to expand our understanding of planet formation.

And since most exoplanets are found by studying transit light curves, any real images of exoplanets are a delight. If the authors of the new study are right, then even a few pixels on an exoplanet’s surface could advance our understanding, just as transit light curves did.

The new study is called “Global Mapping of Exo-Earth Surface Composition Using Sparse Modeling” and is available online at the arxiv preprint site. The lead author is Atsuki Kuwata of the Department of Astronomy at the University of Tokyo.

The study focuses on the future when direct imaging of exoplanets becomes viable. At first, these direct images may only provide a few pixels of the exoplanet’s surface. The question is, how can we learn as much as possible from a few meager pixels? According to this study, more than meets the eye.

In their paper, the team explains that “the time series of light reflected from exoplanets in future direct imaging could provide spatial information about the planet’s surface.”

They used “sparse modeling” to extract information from direct images of exoplanets. Sparse modeling is a machine learning tool that allows you to discover predictive patterns in data,

The researchers applied sparse simulation on what they call “toy Earth.” They revealed surface features useful for studying exoplanets.

“By applying our technique to a cloudless Earth toy model, we have shown that our method can obtain sparse and continuous surface distributions, as well as unmixed spectra, without prior knowledge of the planet’s surface,” the researchers write.

They also applied their technique to real Earth data obtained using DSCOVR/EPIC. DSCOVR is the NOAA Earth Observation Satellite and EPIC is the polychromatic camera on the DSCOVR satellite.

EPIC is a powerful tool that provides detailed measurements of ozone, aerosols, cloud reflectance, cloud height, vegetation properties, and estimates of UV radiation at the Earth’s surface. The researchers “muted” all of this detailed data about the Earth’s surface, as if it were a distant exoplanet they were looking at.

By applying their sparse modeling approach to the DSCOVR/EPIC data, they found patterns that they identified as oceans and cloud cover.

They also found two components that they identified as land. “In addition, we found two components that resemble the distribution of land. One of the components reflects the Sahara desert, and the other roughly corresponds to vegetation, although their spectra are still cloudy.”

Scientists are 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 simulation with Tikhonov’s regularization. “We concluded that sparse simulation gives better inferences about surface distribution and unmixed spectra than the method based on Tikhonov’s regularization,” write the authors.

This study is an improvement on some previous work, and the results are intriguing. One of the obstacles to this kind of work is that the planets rotate. For the results to be reliable, scientists must account for the rotation of the exoplanet with extreme precision.

But the clouds do not sit still while we shoot their portraits from a distance of tens and hundreds of light years.

The study had to make adjustments for this. “Furthermore, we assumed the distribution of the end member surface to be static, but we also need to take into account the dynamic movement of surfaces, especially for clouds,” the team writes in their conclusion.

This work is taking on new significance as telescopes will begin to image exoplanets directly in the future. This is the realm of powerful new ground-based telescopes such as the forthcoming European Extremely Large Telescope (E-ELT) and the Giant Magellanic Telescope (GMT).

These telescopes are very powerful and will produce clearer images than space telescopes. Sharpness is essential for detecting direct light from exoplanets and imaging them.

Currently, direct images of exoplanets do not contain much detail. They are still interesting and in some ways scientifically valuable, but they don’t show surface detail.

Artists is another resource in the field of exoplanet imaging. Skilled illustrators like ESA’s Martin Kornmesser arouse our curiosity and excitement with their data-driven depictions of distant worlds. If Kornmesser and others had not spread information about exoplanets to the general public, we would be in a very different place.

In 2015, GMT project director Patrick McCarthy told Forbes magazine that “we should [also] be able to see Jupiter and Saturn-like planets forming around stars in the Milky Way’s Orion and Taurus star-forming complexes with relative ease.”

But these images will not be crystal clear, and they will not show all the details of the planet’s surface. Scientists will have to extract as much detail as possible from these images using machine learning, modelling, simulation and other tools.


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