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By NASA
ASIA-AQ DC-8 aircraft flies over Bangkok, Thailand to monitor seasonal haze from fire smoke and urban pollution. Photo credit: Rafael Luis Méndez Peña. Tracking the spread of harmful air pollutants across large regions requires aircraft, satellites, and diverse team of scientists. NASA’s global interest in the threat of air pollution extends into Asia, where it works with partners on the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ). This international mission integrates satellite data and aircraft measurements with local air quality ground monitoring and modeling efforts across Asia.
Orchestrating a mission of this scale requires complicated agreements between countries, the coordination of aircraft and scientific instrumentation, and the mobilization of scientists from across the globe. To make this possible, ARC’s Earth Science Project Office (ESPO) facilitated each phase of the campaign, from site preparation and aircraft deployment to sensitive data management and public outreach.
“Successfully meeting the ASIA-AQ mission logistics requirements was an incredible effort in an uncertainty-filled environment and a very constrained schedule to execute and meet those requirements,” explains ASIA-AQ Project Manager Jhony Zavaleta. “Such effort drew on the years long experience on international shipping expertise, heavy equipment operations, networking and close coordination with international service providers and all of the U.S. embassies at each of our basing locations.”
Map of planned ASIA-AQ operational regions. Yellow circles indicate the original areas of interest for flight sampling. The overlaid colormap shows annual average nitrogen dioxide (NO2) concentrations observed by the TROPOMI satellite with red colors indicating the most polluted locations. Understanding Air Quality Globally
ASIA-AQ benefits our understanding of air quality and the factors controlling its daily variability by investigating the ways that air quality can be observed and quantified. The airborne measurements collected during the campaign are directly integrated with existing satellite observations of air quality, local air quality monitoring networks, other available ground assets, and models to provide a level of detail otherwise unavailable to advance understanding of regional air quality and improve future integration of satellite and ground monitoring information.
ESPO’s Mission-Critical Contributions
Facilitating collaboration between governmental agencies and the academic community by executing project plans, navigating bureaucratic hurdles, and consensus building. Mission planning for two NASA aircraft. AFRC DC-8 completed 16 science flights, totaling 125 flight hours. The LaRC GIII completed 35 science flights, totaling 157.7 flight hours. Enabling international fieldwork and workforce mobilization by coordinating travel, securing authorizations and documentation, and maintaining relationships with local research partners. Managing outreach to local governments and schools. ASIA-AQ team members showcased tools used for air quality science to elementary/middle/high school students. Recent news feature here. View of ASIA-AQ aircraft in Bangkok, Thailand. ESPO staff from left to right: Dan Chirica, Marilyn Vasques, Sam Kim, Jhony Zavaleta, and Andrian Liem. Aircraft from left to right: Korean Meteorological Agency/National Institute of Meteorological Sciences, NASA LaRC GIII, NSASA DC-8, (2) Hanseo University, Sunny Air (private aircraft contracted by Korean Meteorological Agency). Photo: Rafael Mendez Peña. The flying laboratory of NASA’s DC-8
NASA flew its DC-8 aircraft, picture above, equipped with instrumentation to monitor the quality, source, and movement of harmful air pollutants. Scientists onboard used the space as a laboratory to analyze data in real-time and share it with a network of researchers who aim to tackle this global issue.
“Bringing the DC-8 flying laboratory and US researchers to Asian countries not only advances atmospheric research but also fosters international scientific collaboration and education,” said ESPO Project Specialist Vidal Salazar. “Running a campaign like ASIA AQ also opens doors for shared knowledge and exposes local communities to cutting-edge research.”
Fostering Partnerships Through Expertise and Goodwill
International collaboration fostered through this campaign contributes to an ongoing dialogue about air pollution between Asian countries.
“NASA’s continued scientific and educational activities around the world are fundamental to building relationships with partnering countries,” said ESPO Director Marilyn Vasques. “NASA’s willingness to share data and provide educational opportunities to locals creates goodwill worldwide.”
The role of ESPO in identifying, strategizing, and executing on project plans across the globe created a path for multi-sectoral community engagement on air quality. These global efforts to improve air quality science directly inform efforts to save lives from this hazard that affects all.
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By European Space Agency
Video: 00:03:29 Mars’s surface is covered in all manner of scratches and scars. Its many marks include the fingernail scratches of Tantalus Fossae, the colossal canyon system of Valles Marineris, the oddly orderly ridges of Angustus Labyrinthus, and the fascinating features captured in today’s video release from Mars Express: the cat scratches of Nili Fossae.
Nili Fossae comprises parallel trenches hundreds of metres deep and several hundred kilometres long, stretching out along the eastern edge of a massive impact crater named Isidis Planitia.
This new video features observations from Mars Express's High Resolution Stereo Camera (HRSC). It first flies northwards towards and around these large trenches, showing their fractured, uneven appearance, before turning back to head southwards. It ends by zooming out to a ‘bird’s eye’ view, with the landing site of NASA’s Perseverance rover, Jezero Crater, visible in the lower-middle part of the final scene. (You can explore this crater further via ESA’s interactive map.)
The trenches of Nili Fossae are actually features known as ‘graben’, which form when the ground sitting between two parallel faults fractures and falls away. As the graben seem to curve around Isidis Planitia, it’s likely that they formed as Mars’s crust settled following the formation of the crater by an incoming space rock hitting the surface. Similar ruptures – the counterpart to Nili Fossae – are found on the other side of the crater, and named Amenthes Fossae.
Scientists have focused on Nili Fossae in recent years due to the impressive amount and diversity of minerals found in this area, including silicates, carbonates, and clays (many of which were discovered by Mars Express’s OMEGA instrument). These minerals form in the presence of water, indicating that this region was very wet in ancient martian history. Much of the ground here formed over 3.5 billion years ago, when surface water was abundant across Mars. Scientists believe that water flowed not only across the surface here but also beneath it, forming underground hydrothermal flows that were heated by ancient volcanoes.
Because of what it could tell us about Mars’s ancient and water-rich past, Nili Fossae was considered as a possible landing site for NASA’s Curiosity rover, before the rover was ultimately sent to Gale Crater in 2012. Another mission, NASA’s Perseverance rover, was later sent to land in the nearby Jezero Crater, visible at the end of this video.
Mars Express has visited Nili Fossae before, imaging the region’s graben system back in 2014. The mission has orbited the Red Planet since 2003, imaging Mars’s surface, mapping its minerals, studying its tenuous atmosphere, probing beneath its crust, and exploring how various phenomena interact in the martian environment. For more from the orbiter and its HRSC, see ESA's Mars Express releases.
Disclaimer: This video is not representative of how Mars Express flies over the surface of Mars. See processing notes below.
Processing notes: The video is centred at 23°N, 78°E. It was created using Mars Chart (HMC30) data, an image mosaic made from single-orbit observations from Mars Express’s HRSC. This mosaic was combined with topography derived from a digital terrain model of Mars to generate a three-dimensional landscape. For every second of the movie, 62.5 separate frames are rendered following a pre-defined camera path. The vertical exaggeration is three-fold. Atmospheric effects – clouds and haze – have been added, and start building up at a distance of 50 km.
Click here for the original video created by Freie Universität Berlin, who use Mars Express data to prepare spectacular views of the martian surface. The original version has no voiceover, captions or ESA logo.
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By European Space Agency
Image: Astronomers have created the largest yet cosmic 3D map of quasars: bright and active centres of galaxies powered by supermassive black holes. This map shows the location of about 1.3 million quasars in space and time, with the furthest shining bright when the Universe was only 1.5 billion years old.
The new map has been made with data from ESA’s Gaia space telescope. While Gaia’s main objective is to map the stars in our own galaxy, in the process of scanning the sky it also spots objects outside the Milky Way, such as quasars and other galaxies.
The graphic representation of the map (bottom right on the infographic) shows us the location of quasars from our vantage point, the centre of the sphere. The regions empty of quasars are where the disc of our galaxy blocks our view.
Quasars are powered by supermassive black holes at the centre of galaxies and can be hundreds of times as bright as an entire galaxy. As the black hole’s gravitational pull spins up nearby gas, the process generates an extremely bright disk, and sometimes jets of light, that telescopes can observe.
The galaxies that quasars live in sit inside massive clouds of invisible dark matter. The distribution of dark matter gives insight into how much dark matter there is in the Universe, and how strong it clusters. Astronomers compare these measurements across cosmic time to test our current model of the Universe's composition and evolution.
Because quasars are so bright, astronomers use them to map out the dark matter in the very distant Universe, and fill in the timeline of how the cosmos evolved.
For example, scientists have already compared the new quasar map with the cosmic microwave background, a snapshot of the oldest light in our cosmos. As this light travels to us, it is bent by the intervening web of dark matter – the same web mapped out by the quasars – and by comparing the two, scientists can measure how strongly matter clumps together through time.
This map was made by Kate Storey-Fisher of the Donostia International Physics Center in Spain and the New York University, USA, and colleagues, and published in the Astrophysical Journal. It uses data from Gaia’s third data release, which contained 6.6 million quasar candidates, as well as data from NASA’s Wide-Field Infrared Survey Explorer (WISE) and the Sloan Digital Sky Survey (SDSS). Combining the datasets helped clean Gaia’s original dataset of contaminants such as stars and galaxies, and better pinpoint the distances to the quasars. The team also created a map of where dust, stars and other phenomena are expected to block our view of some quasars, which is critical for interpreting the quasar map.
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By NASA
The software discipline has broad involvement across each of the NASA Mission Directorates. Some recent discipline focus and development areas are highlighted below, along with a look at the Software Technical Discipline Team’s (TDT) approach to evolving discipline best practices toward the future.
Understanding Automation Risk
Software creates automation. Reliance on that automation is increasing the amount of software in NASA programs. This year, the software team examined historical software incidents in aerospace to characterize how, why, and where software or automation is mostly likely to fail. The goal is to better engineer software to minimize the risk of errors, improve software processes, and better architect software for resilience to errors (or improve fault-tolerance should errors occur).
Some key findings shown in the above charts, indicate that software more often does the wrong thing rather than just crash. Rebooting was found to be ineffective when software behaves erroneously. Unexpected behavior was mostly attributed to the code or logic itself, and about half of those instances were the result of missing software—software not present due to unanticipated situations or missing requirements. This may indicate that even fully tested software is exposed to this significant class of error. Data misconfiguration was a sizeable factor that continues to grow with the advent of more modern data-driven systems. A final subjective category assessed was “unknown unknowns”—things that could not have been reasonably anticipated. These accounted for 19% of software incidents studied.
The software team is using and sharing these findings to improve best practices. More emphasis is being placed on the importance of complete requirements, off-nominal test campaigns, and “test as you fly” using real hardware in the loop. When designing systems for fault tolerance, more consideration should be given to detecting and correcting for erroneous behavior versus just checking for a crash. Less confidence should be placed on rebooting as an effective recovery strategy. Backup strategies for automations should be employed for critical applications—considering the historic prevalence of absent software and unknown unknowns. More information can be found in NASA/TP-20230012154, Software Error Incident Categorizations in Aerospace.
Employing AI and Machine Learning Techniques
The rise of artificial intelligence (AI) and machine learning (ML) techniques has allowed NASA to examine data in new ways that were not previously possible. While NASA has been employing autonomy since its inception, AI/ML techniques provide teams the ability to expand the use of autonomy outside of previous bounds. The Agency has been working on AI ethics frameworks and examining standards, procedures, and practices, taking security implications into account. While AI/ML generally uses nondeterministic statistical algorithms that currently limit its use in safety-critical flight applications, it is used by NASA in more than 400 AI/ML projects aiding research and science. The Agency also uses AI/ML Communities of Practice for sharing knowledge across the centers. The TDT surveyed AI/ML work across the Agency and summarized it for trends and lessons.
Common usages of AI/ML include image recognition and identification. NASA Earth science missions use AI/ML to identify marine debris, measure cloud thickness, and identify wildfire smoke (examples are shown in the satellite images below). This reduces the workload on personnel. There are many applications of AI/ML being used to predict atmospheric physics. One example is hurricane track and intensity prediction. Another example is predicting planetary boundary layer thickness and comparing it against measurements, and those predictions are being fused with live data to improve the performance over previous boundary layer models.
Examples of how NASA uses AI/ML. Satellite images of clouds with estimation of cloud thickness (left) and wildfire detection (right). NASA-HDBK-2203, NASA Software Engineering and Assurance Handbook (https://swehb.nasa.gov) The Code Analysis Pipeline: Static Analysis Tool for IV&V and Software Quality Improvement
The Code Analysis Pipeline (CAP) is an open-source tool architecture that supports software development and assurance activities, improving overall software quality. The Independent Verification and Validation (IV&V) Program is using CAP to support software assurance on the Human Landing System, Gateway, Exploration Ground Systems, Orion, and Roman. CAP supports the configuration and automated execution of multiple static code analysis tools to identify potential code defects, generate code metrics that indicate potential areas of quality concern (e.g., cyclomatic complexity), and execute any other tool that analyzes or processes source code. The TDT is focused on integrating Modified Condition/Decision Coverage analysis support for coverage testing. Results from tools are consolidated into a central database and presented in context through a user interface that supports review, query, reporting, and analysis of results as the code matures.
The tool architecture is based on an industry standard DevOps approach for continuous building of source code and running of tools. CAP integrates with GitHub for source code control, uses Jenkins to support automation of analysis builds, and leverages Docker to create standard and custom build environments that support unique mission needs and use cases.
Improving Software Process & Sharing Best Practices
The TDT has captured the best practice knowledge from across the centers in NPR 7150.2, NASA Software Engineering Requirements, and NASA-HDBK-2203, NASA Software Engineering and Assurance Handbook (https://swehb.nasa.gov.) Two APPEL training classes have been developed and shared with several organizations to give them the foundations in the NPR and software engineering management. The TDT established several subteams to help programs/projects as they tackle software architecture, project management, requirements, cybersecurity, testing and verification, and programmable logic controllers. Many of these teams have developed guidance and best practices, which are documented in NASA-HDBK-2203 and on the NASA Engineering Network.
NPR 7150.2 and the handbook outline best practices over the full lifecycle for all NASA software. This includes requirements development, architecture, design, implementation, and verification. Also covered, and equally important, are the supporting activities/functions that improve quality, including software assurance, safety configuration management, reuse, and software acquisition. Rationale and guidance for the requirements are addressed in the handbook that is internally and externally accessible and regularly updated as new information, tools, and techniques are found and used.
The Software TDT deputies train software engineers, systems engineers, chief engineers, and project managers on the NPR requirements and their role in ensuring these requirements are implemented across NASA centers. Additionally, the TDT deputies train software technical leads on many of the advanced management aspects of a software engineering effort, including planning, cost estimating, negotiating, and handling change management.
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By NASA
These images represent a sample of galaxy clusters that are part of the largest and most complete study to learn what triggers stars to form in the universe’s biggest galaxies. Clusters of galaxies are the largest objects in the universe held together by gravity and contain huge amounts of hot gas seen in X-rays. This research, made using Chandra and other telescopes, showed that the conditions for stellar conception in these exceptionally massive galaxies have not changed over the last ten billion years. In these images, X-rays from Chandra are shown along with optical data from Hubble.X-ray: NASA/CXC/MIT/M. Calzadilla el al.; Optical: NASA/ESA/STScI; Image Processing: NASA/CXC/SAO/N. Wolk & J. Major These four images represent a sample of galaxy clusters that are part of the largest and most complete study to learn what triggers stars to form in the universe’s biggest galaxies, as described in our latest press release. This research, made using NASA’s Chandra X-ray Observatory and other telescopes, showed that the conditions for stellar conception in these exceptionally massive galaxies have not changed over the last ten billion years.
Galaxy clusters are the largest objects in the universe held together by gravity and contain huge amounts of hot gas seen in X-rays. This hot gas weighs several times the total mass of all the stars in all the hundreds of galaxies typically found in galaxy clusters. In the four galaxy cluster images in this graphic, X-rays from hot gas detected by Chandra are in purple and optical data from NASA’s Hubble Space Telescope, mostly showing galaxies in the clusters, are yellow and cyan.
In this study, researchers looked at the brightest and most massive class of galaxies in the universe, called brightest cluster galaxies (BCGs), in the centers of 95 clusters of galaxies. The galaxy clusters chosen are themselves an extreme sample — the most massive clusters in a large survey using the South Pole Telescope (SPT), with funding support from the National Science Foundation and Department of Energy — and are located between 3.4 and 9.9 billion light-years from Earth.
The four galaxy clusters shown here at located at distances of 3.9 billion (SPT-CLJ0106-5943), 5.6 billion (SPT-CLJ0307-6225), 6.4 billion (SPT-CLJ0310-4647) and 7.7 billion (SPT-CLJ0615-5746) light-years from Earth, and the images are 1.7 million, 2 million, 2.4 million and 2.2 million light-years across, respectively. By comparison our galaxy is only about 100,000 light-years across.
In SPT-CLJ0307-6225 the BCG is near the bottom right of the image and in the other images they are near the centers. Some of the long, narrow features are caused by gravitational lensing, where mass in the clusters is warping the light from galaxies behind the clusters. The images have been rotated from standard astronomer’s configuration of North up by 20 degrees clockwise (SPT-CLJ0106-5943), 6.2 degrees counterclockwise (SPT-CLJ0307-6225), 29,2 degrees counterclockwise (SPT-CLJ0310-4647) and 24.2 degrees clockwise (SPT-CLJ0615-5746).
The team found that the precise trigger for stars to form in the galaxies that they studied is when the amount of disordered motion in the hot gas — a physical concept called “entropy” — falls below a critical threshold. Below this threshold, the hot gas inevitably cools to form new stars.
In addition to the X-ray data from Chandra X-ray Observatory and radio data from the SPT already mentioned, this result also used radio data from the Australia Telescope Compact Array, and the Australian SKA Pathfinder Telescope, infrared data from NASA’s WISE satellite, and several optical telescopes. The optical telescopes used in this study were the Magellan 6.5-m Telescopes, the Gemini South Telescope, the Blanco 4-m Telescope (DECam, MOSAIC-II) and the Swope 1m Telescope. A total of almost 50 days of Chandra observing time was used for this result.
Michael Caldazilla of the Massachusetts Institute of Technology (MIT) presented these results at the 243rd meeting of the American Astronomical Society in New Orleans, LA. In addition, there is a paper submitted to The Astrophysical Journal led by Caldazilla on this result (preprint here). The other authors on the paper are Michael McDonald (MIT), Bradford Benson (University of Chicago), Lindsay Bleem (Argonne National Laboratory), Judith Croston (The Open University, UK), Megan Donahue (Michigan State University), Alastair Edge (University of Durham, UK), Gordon Garmire (Penn State University), Julie Hvalacek-Larrondo (University of Colorado), Minh Huynh (CSIRO, Australia), Gourav Khullar (University of Pittsburgh), Ralph Kraft (Center for Astrophysics | Harvard & Smithsonian), Brian McNamara (University of Waterloo, Canada), Allison Noble (Arizona State University), Charles Romero (CfA), Florian Ruppin (University of Lyon, France), Taweewat Somboonpanyakul (Stanford University), and Mark Voit (Michigan State).
NASA’s Marshall Space Flight Center manages the Chandra program. The Smithsonian Astrophysical Observatory’s Chandra X-ray Center controls science operations from Cambridge, Massachusetts, and flight operations from Burlington, Massachusetts.
Read more from NASA’s Chandra X-ray Observatory.
For more Chandra images, multimedia and related materials, visit:
https://www.nasa.gov/mission/chandra-x-ray-observatory/
Visual Description
This release includes composite images of four galaxy clusters, presented in a two-by-two grid. Each image features a hazy, purple cloud representing X-rays from hot gas observed by Chandra. The distant galaxies in and around the clouds of hot gas have been captured in optical data, and are shown in golden yellows with hints of vibrant cyan blue.
The galaxy cluster at our upper left is labeled SPT-CLJ0310-4647. Here, the blackness of space is packed with gleaming specks of white, golden yellow, and bright blue light. These are individual galaxies. Some of the galaxies resemble blurred, glowing dots. In other galaxies, the curving arms of a spiral formation are discernible. At the center of the image, a faint purple cloud surrounds several of the cluster’s brightest galaxies.
At our upper right is an image of SPT-CLJ0615-5746. This is the most distant cluster of the four so the galaxies it contains appear relatively small. These galaxies are mostly located near the center of the image. The purple cloud of hot gas is roughly spherical, and has a light purple spot at its core.
At our lower right is SPT-CLJ0307-6225. Here, X-rays from hot gas are represented by a large, misty, purple cloud that covers much of the image. The brightest spot in the cloud is a light purple dot near our lower right. The most notable galaxy in this image is a pixilated spiral galaxy above and to our left of center.
The galaxy cluster at our lower left is labeled SPT-CLJ0106-5943. This cluster features a scattering of cyan blue galaxies, several of which appear stretched or elongated due to gravitational lensing. At the center of the image is a purple gas cloud with a bright white speck at its core.
News Media Contact
Megan Watzke
Chandra X-ray Center
Cambridge, Mass.
617-496-7998
Jonathan Deal
Marshall Space Flight Center
Huntsville, Ala.
256-544-0034
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