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By NASA
NASA’s Dawn spacecraft captured this image of Vesta as it left the giant asteroid’s orbit in 2012. The framing camera was looking down at the north pole, which is in the middle of the image.NASA/JPL-Caltech/UCLA/MPS/DLR/IDA Known as flow formations, these channels could be etched on bodies that would seem inhospitable to liquid because they are exposed to the extreme vacuum conditions of space.
Pocked with craters, the surfaces of many celestial bodies in our solar system provide clear evidence of a 4.6-billion-year battering by meteoroids and other space debris. But on some worlds, including the giant asteroid Vesta that NASA’s Dawn mission explored, the surfaces also contain deep channels, or gullies, whose origins are not fully understood.
A prime hypothesis holds that they formed from dry debris flows driven by geophysical processes, such as meteoroid impacts, and changes in temperature due to Sun exposure. A recent NASA-funded study, however, provides some evidence that impacts on Vesta may have triggered a less-obvious geologic process: sudden and brief flows of water that carved gullies and deposited fans of sediment. By using lab equipment to mimic conditions on Vesta, the study, which appeared in Planetary Science Journal, detailed for the first time what the liquid could be made of and how long it would flow before freezing.
Although the existence of frozen brine deposits on Vesta is unconfirmed, scientists have previously hypothesized that meteoroid impacts could have exposed and melted ice that lay under the surface of worlds like Vesta. In that scenario, flows resulting from this process could have etched gullies and other surface features that resemble those on Earth.
To explore potential explanations for deep channels, or gullies, seen on Vesta, scientists used JPL’s Dirty Under-vacuum Simulation Testbed for Icy Environments, or DUSTIE, to simulate conditions on the giant asteroid that would occur after meteoroids strike the surface.NASA/JPL-Caltech But how could airless worlds — celestial bodies without atmospheres and exposed to the intense vacuum of space — host liquids on the surface long enough for them to flow? Such a process would run contrary to the understanding that liquids quickly destabilize in a vacuum, changing to a gas when the pressure drops.
“Not only do impacts trigger a flow of liquid on the surface, the liquids are active long enough to create specific surface features,” said project leader and planetary scientist Jennifer Scully of NASA’s Jet Propulsion Laboratory in Southern California, where the experiments were conducted. “But for how long? Most liquids become unstable quickly on these airless bodies, where the vacuum of space is unyielding.”
The critical component turns out to be sodium chloride — table salt. The experiments found that in conditions like those on Vesta, pure water froze almost instantly, while briny liquids stayed fluid for at least an hour. “That’s long enough to form the flow-associated features identified on Vesta, which were estimated to require up to a half-hour,” said lead author Michael J. Poston of the Southwest Research Institute in San Antonio.
Launched in 2007, the Dawn spacecraft traveled to the main asteroid belt between Mars and Jupiter to orbit Vesta for 14 months and Ceres for almost four years. Before ending in 2018, the mission uncovered evidence that Ceres had been home to a subsurface reservoir of brine and may still be transferring brines from its interior to the surface. The recent research offers insights into processes on Ceres but focuses on Vesta, where ice and salts may produce briny liquid when heated by an impact, scientists said.
Re-creating Vesta
To re-create Vesta-like conditions that would occur after a meteoroid impact, the scientists relied on a test chamber at JPL called the Dirty Under-vacuum Simulation Testbed for Icy Environments, or DUSTIE. By rapidly reducing the air pressure surrounding samples of liquid, they mimicked the environment around fluid that comes to the surface. Exposed to vacuum conditions, pure water froze instantly. But salty fluids hung around longer, continuing to flow before freezing.
The brines they experimented with were a little over an inch (a few centimeters) deep; scientists concluded the flows on Vesta that are yards to tens of yards deep would take even longer to refreeze.
The researchers were also able to re-create the “lids” of frozen material thought to form on brines. Essentially a frozen top layer, the lids stabilize the liquid beneath them, protecting it from being exposed to the vacuum of space — or, in this case the vacuum of the DUSTIE chamber — and helping the liquid flow longer before freezing again.
This phenomenon is similar to how on Earth lava flows farther in lava tubes than when exposed to cool surface temperatures. It also matches up with modeling research conducted around potential mud volcanoes on Mars and volcanoes that may have spewed icy material from volcanoes on Jupiter’s moon Europa.
“Our results contribute to a growing body of work that uses lab experiments to understand how long liquids last on a variety of worlds,” Scully said.
Find more information about NASA’s Dawn mission here:
https://science.nasa.gov/mission/dawn/
News Media Contacts
Gretchen McCartney
Jet Propulsion Laboratory, Pasadena, Calif.
818-287-4115
gretchen.p.mccartney@jpl.nasa.gov
Karen Fox / Molly Wasser
NASA Headquarters, Washington
202-358-1600
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov
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Last Updated Dec 20, 2024 Related Terms
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By European Space Agency
Global warming is driving the rapid melting of the Greenland Ice Sheet, contributing to global sea level rise and disrupting weather patterns worldwide. Because of this, precise measurements of its changing shape are of critical importance for adapting to climate change.
Now, scientists have delivered the first measurements of the Greenland Ice Sheet’s changing shape using data from ESA's CryoSat and NASA's ICESat-2 ice missions.
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By NASA
Download PDF: Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
Energy modulators (EM), also known as energy absorbers, are safety-critical components that are used to control shocks and impulses in a load path. EMs are textile devices typically manufactured out of nylon, Kevlar® and other materials, and control loads by breaking rows of stitches that bind a strong base webbing together as shown in Figure 1. A familiar EM application is a fall-protection harness used by workers to prevent injury from shock loads when the harness arrests a fall. EMs are also widely used in parachute systems to control shock loads experienced during the various stages of parachute system deployment.
Random forest is an innovative algorithm for data classification used in statistics and machine learning. It is an easy to use and highly flexible ensemble learning method. The random forest algorithm is capable of modeling both categorical and continuous data and can handle large datasets, making it applicable in many situations. It also makes it easy to evaluate the relative importance of variables and maintains accuracy even when a dataset has missing values.
Random forests model the relationship between a response variable and a set of predictor or independent variables by creating a collection of decision trees. Each decision tree is built from a random sample of the data. The individual trees are then combined through methods such as averaging or voting to determine the final prediction (Figure 2). A decision tree is a non-parametric supervised learning algorithm that partitions the data using a series of branching binary decisions. Decision trees inherently identify key features of the data and provide a ranking of the contribution of each feature based on when it becomes relevant. This capability can be used to determine the relative importance of the input variables (Figure 3). Decision trees are useful for exploring relationships but can have poor accuracy unless they are combined into random forests or other tree-based models.
The performance of a random forest can be evaluated using out-of-bag error and cross-validation techniques. Random forests often use random sampling with replacement from the original dataset to create each decision tree. This is also known as bootstrap sampling and forms a bootstrap forest. The data included in the bootstrap sample are referred to as in-the-bag, while the data not selected are out-of-bag. Since the out-of-bag data were not used to generate the decision tree, they can be used as an internal measure of the accuracy of the model. Cross-validation can be used to assess how well the results of a random forest model will generalize to an independent dataset. In this approach, the data are split into a training dataset used to generate the decision trees and build the model and a validation dataset used to evaluate the model’s performance. Evaluating the model on the independent validation dataset provides an estimate of how accurately the model will perform in practice and helps avoid problems such as overfitting or sampling bias. A good model performs well on
both the training data and the validation data.
The complex nature of the EM system made it difficult for the team to identify how various parameters influenced EM behavior. A bootstrap forest analysis was applied to the test dataset and was able to identify five key variables associated with higher probability of damage and/or anomalous behavior. The identified key variables provided a basis for further testing and redesign of the EM system. These results also provided essential insight to the investigation and aided in development of flight rationale for future use cases.
For information, contact Dr. Sara R. Wilson. sara.r.wilson@nasa.gov
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By European Space Agency
Video: 00:04:04 English Paxi explores ice
Join Paxi on an adventure to the North and South poles, to learn more about ice and its role in keeping Earth cool.
Italian Paxi osserva il ghiaccio
Unisciti a Paxi in un'avventura ai poli Nord e Sud, per saperne di più sul ghiaccio e sul suo ruolo nel mantenere la Terra fresca.
German Paxi erforscht das Eis
Begleiten Sie Paxi auf ein Abenteuer zum Nord- und Südpol, um mehr über Eis und seine Rolle bei der Kühlung der Erde zu erfahren.
French Paxi explore la glace
Rejoignez Paxi dans une aventure aux pôles Nord et Sud, pour en savoir plus sur la glace et son rôle dans le refroidissement de la Terre.
Spanish Paxi explora el hielo
Únete a Paxi en una aventura a los polos Norte y Sur, para aprender más sobre el hielo y su papel en mantener la Tierra fría.
Portuguese Paxi explora o gelo
Junte-se a Paxi numa aventura aos pólos Norte e Sul, para aprender mais sobre o gelo e o seu papel na manutenção da Terra fresca.
Greek Ο Πάξι εξερευνά τον πάγο
Ελάτε μαζί με τον Paxi σε μια περιπέτεια στο Βόρειο και το Νότιο Πόλο, για να μάθετε περισσότερα για τον πάγο και το ρόλο του στη διατήρηση της ψύξης της Γης.
Polish Paxi bada lód
Dołącz do Paxi podczas przygody na biegunie północnym i południowym, aby dowiedzieć się więcej o lodzie i jego roli w chłodzeniu Ziemi.
Swedish Paxi utforskar is
Följ med Paxi på ett äventyr till Nord- och Sydpolen för att lära dig mer om is och dess roll för att hålla jorden sval.
Norwegian Paxi utforsker is
Bli med Paxi på et eventyr til Nord- og Sydpolen for å lære mer om is og dens rolle i å holde jorden kjølig.
Danish Paxi udforsker is
Tag med Paxi på eventyr til Nord- og Sydpolen for at lære mere om is og dens rolle i at holde Jorden kølig.
Romanian Paxi explorează gheață
Alăturați-vă lui Paxi într-o aventură la polii Nord și Sud, pentru a afla mai multe despre gheață și rolul său în menținerea Pământului rece.
Finnish Paxi tutkii jäätä
Lähde Paxin mukaan seikkailulle pohjois- ja etelänavoille ja opi lisää jäästä ja sen roolista maapallon viileänä pitämisessä.
Estonian Paxi avastab jääd
Liitu Paxiga seiklusel põhja- ja lõunapoolusele, et õppida rohkem jääst ja selle rollist Maa jahedana hoidmisel.
Czech Paxi zkoumá led
Vydejte se s Paxi na dobrodružnou výpravu na severní a jižní pól, abyste se dozvěděli více o ledu a jeho úloze při udržování chladu na Zemi.
Dutch Paxi onderzoekt ijs
Ga mee met Paxi op avontuur naar de Noord- en Zuidpool om meer te leren over ijs en de rol die ijs speelt bij het koel houden van de aarde.
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By NASA
At Goddard Space Flight Center, the GSFC Data Science Group has completed the testing for their SatVision Top-of-Atmosphere (TOA) Foundation Model, a geospatial foundation model for coarse-resolution all-sky remote sensing imagery. The team, comprised of Mark Carroll, Caleb Spradlin, Jordan Caraballo-Vega, Jian Li, Jie Gong, and Paul Montesano, has now released their model for wide application in science investigations.
Foundation models can transform the landscape of remote sensing (RS) data analysis by enabling the pre-training of large computer-vision models on vast amounts of remote sensing data. These models can be fine-tuned with small amounts of labeled training and applied to various mapping and monitoring applications. Because most existing foundation models are trained solely on cloud-free satellite imagery, they are limited to applications of land surface or require atmospheric corrections. SatVision-TOA is trained on all-sky conditions which enables applications involving atmospheric variables (e.g., cloud or aerosol).
SatVision TOA is a 3 billion parameter model trained on 100 million images from Moderate Resolution Imaging Spectroradiometer (MODIS). This is, to our knowledge, the largest foundation model trained solely on satellite remote sensing imagery. By including “all-sky” conditions during pre-training, the team incorporated a range of cloud conditions often excluded in traditional modeling. This enables 3D cloud reconstruction and cloud modeling in support of Earth and climate science, offering significant enhancement for large-scale earth observation workflows.
With an adaptable and scalable model design, SatVision-TOA can unify diverse Earth observation datasets and reduce dependency on task-specific models. SatVision-TOA leverages one of the largest public datasets to capture global contexts and robust features. The model could have broad applications for investigating spectrometer data, including MODIS, VIIRS, and GOES-ABI. The team believes this will enable transformative advancements in atmospheric science, cloud structure analysis, and Earth system modeling.
The model architecture and model weights are available on GitHub and Hugging Face, respectively. For more information, including a detailed user guide, see the associated white paper: SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery.
Examples of image reconstruction by SatVision-TOA. Left: MOD021KM v6.1 cropped image chip using MODIS bands [1, 3, 2]. Middle: The same images with randomly applied 8×8 mask patches, masking 60% of the original image. Right: The reconstructed images produced by the model, along with their respective Structural Similarity Index Measure (SSIM) scores. These examples illustrate the model’s ability to preserve structural detail and reconstruct heterogeneous features, such as cloud textures and land-cover transitions, with high fidelity.NASAView the full article
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