Permanently shadowed lunar craters contain water ice, but are difficult to
image. A machine learning algorithm now provides sharper images.
The Moon’s polar regions are home to craters and other depressions that
never receive sunlight. Today, a group of researchers led by the Max Planck
Institute for Solar System Research (MPS) in Germany present the
highest-resolution images to date covering 17 such craters. Craters of this
type could contain frozen water, making them attractive targets for future
lunar missions, and the researchers focused further on relatively small and
accessible craters surrounded by gentle slopes. In fact, three of the
craters have turned out to lie within the just-announced mission area of
NASA’s Volatiles Investigating Polar Exploration Rover (VIPER), which is
scheduled to touch down on the Moon in 2023. Imaging the interior of
permanently shadowed craters is difficult, and efforts so far have relied on
long exposure times resulting in smearing and lower resolution. By taking
advantage of reflected sunlight from nearby hills and a novel image
processing method, the researchers have now produced images at 1-2 meters
per pixel, which is at or very close to the best capability of the cameras.
The Moon is a cold, dry desert. Unlike the Earth, it is not surrounded by a
protective atmosphere and water which existed during the Moon’s formation
has long since evaporated under the influence of solar radiation and escaped
into space. Nevertheless, craters and depressions in the polar regions give
some reason to hope for limited water resources. Scientists from MPS, the
University of Oxford and the NASA Ames Research Center have now taken a
closer look at some of these regions.
“Near the lunar north and south poles, the incident sunlight enters the
craters and depressions at a very shallow angle and never reaches some of
their floors”, MPS-scientist Valentin Bickel, first author of the new paper
in Nature Communications, explains. In this “eternal night,” temperatures in
some places are so cold that frozen water is expected to have lasted for
millions of years. Impacts from comets or asteroids could have delivered it,
or it could have been outgassed by volcanic eruptions, or formed by the
interaction of the surface with the solar wind. Measurements of neutron flux
and infrared radiation obtained by space probes in recent years indicate the
presence of water in these regions. Eventually, NASA’s Lunar Crater
Observation and Sensing Satellite (LCROSS) provided direct proof: twelve
years ago, the probe fired a projectile into the shadowed south pole crater
Cabeus. As later analysis showed, the dust cloud emitted into space
contained a considerable amount of water.
However, permanently shadowed regions are not only of scientific interest.
If humans are to ever spend extended periods of time on the Moon, naturally
occurring water will be a valuable resource – and shadowed craters and
depressions will be an important destination. NASA’s uncrewed VIPER rover,
for example, will explore the South Pole region in 2023 and enter such
craters. In order to get a precise picture of their topography and geology
in advance – for mission planning purposes, for example – images from space
probes are indispensable. NASA’s Lunar Reconnaissance Orbiter (LRO) has been
providing such images since 2009.
However, capturing images within the deep darkness of permanently shadowed
regions is exceptionally difficult; after all, the only sources of light are
scattered light, such as that reflecting off the Earth and the surrounding
topography, and faint starlight. “Because the spacecraft is in motion, the
LRO images are completely blurred at long exposure times,” explains Ben
Moseley of the University of Oxford, a co-author of the study. At short
exposure times, the spatial resolution is much better. However, due to the
small amounts of light available, these images are dominated by noise,
making it hard to distinguish real geological features.
To address this problem, the researchers have developed a machine learning
algorithm called HORUS (Hyper-effective nOise Removal U-net Software) that
“cleans up” such noisy images. It uses more than 70,000 LRO calibration
images taken on the dark side of the Moon as well as information about
camera temperature and the spacecraft’s trajectory to distinguish which
structures in the image are artifacts and which are real. This way, the
researchers can achieve a resolution of about 1-2 meters per pixel, which is
five to ten times higher than the resolution of all previously available
images.
Using this method, the researchers have now re-evaluated images of 17
shadowed regions from the lunar south pole region which measure between 0.18
and 54 square kilometers in size. In the resulting images, small geological
structures only a few meters across can be discerned much more clearly than
before. These structures include boulders or very small craters, which can
be found everywhere on the lunar surface. Since the Moon has no atmosphere,
very small meteorites repeatedly fall onto its surface and create such
mini-craters.
“With the help of the new HORUS images, it is now possible to understand the
geology of lunar shadowed regions much better than before,” explains
Moseley. For example, the number and shape of the small craters provide
information about the age and composition of the surface. It also makes it
easier to identify potential obstacles and hazards for rovers or astronauts.
In one of the studied craters, located on the Leibnitz Plateau, the
researchers discovered a strikingly bright mini-crater. “Its comparatively
bright color may indicate that this crater is relatively young,” says
Bickel. Because such a fresh scar provides fairly unhindered insight into
deeper layers, this site could be an interesting target for future missions,
the researchers suggest.
The new images do not provide evidence of frozen water on the surface, such
as bright patches. “Some of the regions we’ve targeted might be slightly too
warm,” Bickel speculates. It is likely that lunar water does not exist as a
clearly visible deposit on the surface at all – instead, it could be
intermixed with the regolith and dust, or may be hidden underground.
To address this and other questions, the researchers’ next step is to use
HORUS to study as many shadowed regions as possible. “In the current
publication, we wanted to show what our algorithm can do. Now we want to
apply it as comprehensively as possible,” says Bickel.
Reference:
Peering into lunar permanently shadowed regions with deep learning” by V. T.
Bickel, B. Moseley, I. Lopez-Francos and M. Shirley, 23 September 2021,
Nature Communications.
DOI: 10.1038/s41467-021-25882-z
Tags:
Space & Astrophysics
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