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Solar System fingerprints found in memories of ESA flotilla
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By European Space Agency
Don’t miss the final ESA Impact of the year!
Your interactive gateway to the most captivating stories and stunning visuals from ESA.
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By NASA
Illustration of the main asteroid belt, orbiting the Sun between Mars and JupiterNASA NASA’s powerful James Webb Space Telescope includes asteroids on its list of objects studied and secrets revealed.
A team led by researchers at the Massachusetts Institute of Technology (MIT) in Cambridge repurposed Webb’s observations of a distant star to reveal a population of small asteroids — smaller than astronomers had ever detected orbiting the Sun in the main asteroid belt between Mars and Jupiter.
The 138 new asteroids range from the size of a bus to the size of a stadium — a size range in the main belt that has not been observable with ground-based telescopes. Knowing how many main belt asteroids are in different size ranges can tell us something about how asteroids have been changed over time by collisions. That process is related to how some of them have escaped the main belt over the solar system’s history, and even how meteorites end up on Earth.
“We now understand more about how small objects in the asteroid belt are formed and how many there could be,” said Tom Greene, an astrophysicist at NASA’s Ames Research Center in California’s Silicon Valley and co-author on the paper presenting the results. “Asteroids this size likely formed from collisions between larger ones in the main belt and are likely to drift towards the vicinity of Earth and the Sun.”
Insights from this research could inform the work of the Asteroid Threat Assessment Project at Ames. ATAP works across disciplines to support NASA’s Planetary Defense Coordination Office by studying what would happen in the case of an Earth impact and modeling the associated risks.
“It’s exciting that Webb’s capabilities can be used to glean insights into asteroids,” said Jessie Dotson, an astrophysicist at Ames and member of ATAP. “Understanding the sizes, numbers, and evolutionary history of smaller main belt asteroids provides important background about the near-Earth asteroids we study for planetary defense.”
Illustration of the James Webb Space TelescopeNASA The team that made the asteroid detections, led by research scientist Artem Burdanov and professor of planetary science Julien de Wit, both of MIT, developed a method to analyze existing Webb images for the presence of asteroids that may have been inadvertently “caught on film” as they passed in front of the telescope. Using the new image processing technique, they studied more than 10,000 images of the star TRAPPIST-1, originally taken to search for atmospheres around planets orbiting the star, in the search for life beyond Earth.
Asteroids shine more brightly in infrared light, the wavelength Webb is tuned to detect, than in visible light, helping reveal the population of main belt asteroids that had gone unnoticed until now. NASA will also take advantage of that infrared glow with an upcoming mission, the Near-Earth Object (NEO) Surveyor. NEO Surveyor is the first space telescope specifically designed to hunt for near-Earth asteroids and comets that may be potential hazards to Earth.
The paper presenting this research, “Detections of decameter main-belt asteroids with JWST,” was published Dec. 9 in Nature.
The James Webb Space Telescope is the world’s premier space science observatory. Webb is solving mysteries in our solar system, looking beyond to distant worlds around other stars, and probing the mysterious structures and origins of our universe and our place in it. Webb is an international program led by NASA with its partners, ESA (European Space Agency) and CSA (Canadian Space Agency).
For news media:
Members of the news media interested in covering this topic should reach out to the NASA Ames newsroom.
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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 European Space Agency
Video: 00:10:27 In 1975, 10 European countries came together with a vision to collaborate on key space activities: science and astronomy, launch capabilities and space applications: the European Space Agency, ESA, was born.
In 2025, we mark half a century of joint European achievement – filled with firsts and breakthroughs in science, exploration and technology, and the space infrastructure and economy that power Europe today.
During the past five decades ESA has grown, developing ever bolder and bigger projects and adding more Member States, with Slovenia joining as the latest full Member State in January.
We’ll also celebrate the 50th anniversary of ESA’s Estrack network, 30 years of satellite navigation in Europe and 20 years since ESA launched the first demonstration satellite Giove-A which laid the foundation for the EU’s own satnav constellation Galileo. Other notable celebrations are the 20th anniversary of ESA’s Business Incubation Centres, or BICs, and the 30th year in space for SOHO, the joint ESA and NASA Solar and Heliospheric Observatory.
Sadly though, 2025 will mean end of science operations for Integral and Gaia. Integral, ESA's gamma-ray observatory has exotic objects in space since 2002 and Gaia concludes a decade of mapping the stars. But as some space telescopes retire, another one provides its first full data release. Launched in 2023, we expect Euclid’s data release early in the new year.
Launch-wise, we’re looking forward to Copernicus Sentinel-4 and -5 (Sentinel-4 will fly on an MTG-sounder satellite and Sentinel-5 on the MetOp-SG-A1 satellite), Copernicus Sentinel-1D, Sentinel-6B and Biomass. We’ll also launch the SMILE mission, or Solar wind Magnetosphere Ionosphere Link Explorer, a joint mission with the Chinese academy of science.
The most powerful version of Europe’s new heavy-lift rocket, Ariane 6, is set to fly operationally for the first time in 2025. With several European commercial launcher companies planning to conduct their first orbital launches in 2025 too, ESA is kicking off the European Launcher Challenge to support the further development of European space transportation industry.
In human spaceflight, Polish ESA project astronaut Sławosz Uznański will fly to the ISS on the commercial Axiom-4 mission. Artemis II will be launched with the second European Service Module, on the first crewed mission around the Moon since 1972.
The year that ESA looks back on a half century of European achievement will also be one of key decisions on our future. At the Ministerial Council towards the end of 2025, our Member States will convene to ensure that Europe's crucial needs, ambitions and the dreams that unite us in space become reality.
So, in 2025, we’ll celebrate the legacy of those who came before but also help establish a foundation for the next 50 years. Join us as we look forward to a year that honours ESA’s legacy and promises new milestones in space.
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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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