
New Study Reveals How Solar Coronal Holes Spray Solar Wind Like A Sun Garden Hose
Scientists from Skolkovo Institute of Science and Technology, together with scientists from the University of Graz, Kanzelhöhe Observatory, and Columbia University, have discovered how coronal holes — vast magnetic windows in the Sun's corona — launch fast solar wind streams into space at supersonic speeds, shaping their flow throughout the heliosphere. These findings set the stage for the upcoming Vigil mission to Lagrange point L5 — a dedicated solar sentinel that will monitor our dynamic Sun, transforming deep-space observations into unprecedented early warnings of solar storms to protect critical infrastructure on Earth and in orbit. The study's findings are published in Scientific Reports, Nature.
The Sun doesn't just shine — it blows. A relentless stream of charged particles, known as the solar wind, surges outward at hundreds of kilometers per second, drenching Earth and the entire solar system in a flood of electrons, protons, and helium nuclei. But this isn't a smooth breeze — it's a turbulent river with fast and slow currents that spark dazzling auroras and disruptive geomagnetic storms. The fastest streams come from coronal holes — dark, cooler patches in the Sun's outer atmosphere where magnetic fields stretch open and high-speed solar wind streams can escape from the Sun into interplanetary space. Yet how exactly these holes shape the solar wind's behavior remains an open question. When high-speed solar wind streams collide with slower solar wind, they create massive structures called corotating interaction regions that spiral outward as the Sun rotates. Since the Sun rotates every 27 days, a single coronal hole can bombard us repeatedly — a celestial metronome of space weather.
A pioneering study led by solar physicists has revealed how coronal holes propel fast solar wind streams of charged particles that race across our solar system. The research also delivers a major advance in space weather forecasting, extending prediction lead times from hours to days. Using a unique observational vantage point at the L5 Lagrange point (60° behind Earth in orbit), scientists can now better predict when these solar winds will reach Earth. The team solved a key puzzle — why solar wind measurements differ between L5 and Earth-orbiting L1 observatories. They traced the variations to three critical factors — the combined effect of smaller coronal holes, their precise locations on the Sun's surface, and the latitudinal position of spacecraft detecting the solar wind. These findings underscore the importance of future missions to L5 and L4 Lagrange points, like ESA's Vigil, to improve early warnings for geomagnetic storms — helping protect satellites, aviation, and power grids from disruptive space weather.
'Imagine watering your garden with a hose,' explains lead author Associate Professor Tatiana Podladchikova, who heads the Engineering Center at Skoltech. 'If you stand directly in front of the stream, you get hit hard. But if you're off to the side, you only catch splashes. This 'garden hose effect' explains why satellites directly aligned with a solar wind stream measure higher speeds than those at an angle. Our study shows this effect is most pronounced for smaller coronal holes at higher solar latitudes, and depends strongly on the latitudinal separation between spacecraft. In contrast, larger coronal holes deliver solar wind more uniformly across the heliosphere.'
These findings will not only improve space weather forecasting and advance the fundamental understanding of the solar-terrestrial environment but also underscore the importance of continued exploration from diverse vantage points like L5 and L4 to fully unravel the Sun's influence on the Solar System, enriching the broader field of heliophysics and space exploration.
Skoltech is a private international university in Russia, cultivating a new generation of leaders in technology, science, and business. As a factory of technologies, it conducts research in breakthrough fields and promotes technological innovation to solve critical problems that face Russia and the world. Skoltech focuses on six priority areas: life sciences, health, and agro; telecommunications, photonics, and quantum technologies; artificial intelligence; advanced materials and engineering; energy efficiency and the energy transition; and advanced studies. Established in 2011 in collaboration with the Massachusetts Institute of Technology (MIT), Skoltech was listed among the world's top 100 young universities by the Nature Index in its both editions (2019, 2021). On Research.com, the Institute ranks as Russian university No. 2 overall and No. 1 for genetics and materials science. In the recent SCImago Institutions Rankings, Skoltech placed first nationwide for computer science. Website: https://www.skoltech.ru/
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Scoop
31-05-2025
- Scoop
Earth's Seasonal Rhythms Are Changing, Putting Species And Ecosystems At Risk
Seasonality shapes much of life on Earth. Most species, including humans, have synchronised their own rhythms with those of Earth's seasons. Plant growth cycles, the migration of billions of animals, and even aspects of human culture – from harvest rituals to Japanese cherry blossom viewings – are dictated by these dominant rhythms. However, climate change and many other human impacts are altering Earth's cycles. While humans can adapt their behaviour by shifting the timing of crop harvests or Indigenous fire-burning practices, species are less able to adapt through evolution or range shifts. Our new research highlights how the impacts of shifting seasons can cascade through ecosystems, with widespread repercussions that may be greater than previously thought. This puts species and ecosystems at risk the world over. We are still far from having a full picture of what changes in seasonality mean for the future of biodiversity. Almost every ecosystem on Earth has seasons From tropical forests to polar ice caps and abyssal depths, the annual journey of Earth around the Sun brings distinct seasons to all corners of the planet. These seasonal rhythms shape ecosystems everywhere, whether through monsoonal rains in equatorial regions or the predictable melt of snowpack in mountain ranges. But the seasonality of these processes is changing rapidly due to local human impacts. This includes dams in many rivers, which completely and abruptly disrupt their natural flow, and deforestation, which changes the timing of the onset of the rain season. These local influences are compounded by climate change, which is systematically modifying seasonal patterns in snow cover, temperature and rainfall around the world. From the earlier seasonal melting of glaciers and the snowpack to the disruption of monsoonal rain cycles, the effects of these changes are being felt widely. Many important ecological processes we rely on could be affected. A mismatch between plankton blooms and the life cycles of fish could affect the health of fisheries. Tourism dependent on seasonal migrations of large mammals could suffer. Even the regulation of the climate system itself is tightly controlled by seasonal processes. Changing seasonality threatens to destabilise key ecological processes and human society. Evolutionary adaptations to seasonal fluctuations The seasonal rhythms of ecosystems are obvious to any observer. The natural timing of annual flowers and deciduous trees – tuned to match seasonal variations in rainfall, temperature and solar radiation – transforms the colours of whole landscapes throughout the year. The arrival and departure of migratory birds, the life cycle of insects and amphibians, and the mating rituals of large mammals can completely change the soundscapes with the seasons. These examples illustrate how seasonality acts as a strong evolutionary force that has shaped the life cycles and behaviour of most species. But, in the face of unprecedented changes to Earth's natural rhythms, these adaptations can lead to complex negative impacts. For instance, snowshoe hares change coat colour between winter and summer to blend in with their surroundings and hide from predators. They are struggling to adapt to shifts in the timing of the first snow and snowmelt. The impact of changing seasonality on hare populations is linked with changes in predation rates. But predators themselves may also be out of sync with the new onset of seasons. Our research highlights that these kinds of complex interactions can propagate impacts through ecosystems, linking individual species' seasonal adaptations to broader food web dynamics, or even ecosystem functions such as carbon sequestration. Although biologists have studied seasonal processes for centuries, we know surprisingly little about how they mediate any ecological impacts of altered seasonality. Our findings show we are likely underestimating these impacts. The distinct mechanisms involved deserve further attention. Until we account for these complex processes, we risk overlooking important ecological and human consequences. The more we understand, the better prepared we are Understanding the extent to which impacts of altered seasonality can interact and propagate from individuals to whole ecosystems is a big challenge. It will require different types of research, complex mathematical modelling and the design of new experiments. But it is not easy to manipulate the seasons in an experiment. Scientists have come up with inventive ways of experimentally testing the effects of altered seasonality. This includes manually removing snow early in spring, manipulating rainfall patterns through irrigation and moving plants and animals to places with different seasonality. Some researchers have even recovered seeds from centuries-old collections to sprout them and look at how recent changes in climate have affected plant populations. These efforts will be of great value for forecasting impacts and designing effective management strategies beneficial for ecosystems and humans alike. Such efforts help to anticipate future shocks and prioritise interventions. For instance, understanding the mechanisms that allow native and non-native species to anticipate seasonal changes has proven useful for ' tricking ' non-native plants into sprouting only in the wrong season. This gives an advantage to native plants. Similarly, studies on the molecular mechanisms involved in the response to seasonality can help us determine whether certain species are likely to adapt to further changes in seasonal patterns. This research can also point out genes that could be targeted for improving the resilience and productivity of crops. Not only are we likely underestimating the ecological risks of shifting seasons, we tend to forget how much our everyday lives depend on them. As Earth's rhythms change, the risks multiply. But so does our opportunity to better understand, anticipate and adapt to these changes. Disclosure statement Daniel Hernández Carrasco receives funding from a Doctoral Scholarship by the University of Canterbury. Jonathan Tonkin receives funding from a Rutherford Discovery Fellowship administered by the Royal Society Te Apārangi and the Centres of Research Excellence Bioprotection Aotearoa and Te Pūnaha Matatini.


Techday NZ
14-05-2025
- Techday NZ
OpenAI reveals how deep research transforms inquiry
OpenAI has introduced a new agentic AI system called 'deep research,' designed to handle complex, time-consuming research tasks by simulating the work of a human analyst. Presented by researchers Isa Fulford and Edward Sun during an OpenAI forum event, the new tool is powered by a fine-tuned version of OpenAI's upcoming O3 model and leverages advanced reasoning and browsing capabilities. "Deep research is an agent in ChatGPT that can do work for you independently," Fulford explained. "You give it a prompt, and it will find, analyse, and synthesise hundreds of online sources to create a comprehensive report at the level of a research analyst." The system is intended to help users across a range of sectors—from academia and medicine to business and software development. "Members are finding that deep research asks clarifying questions to refine research before it even starts," said Fulford. "We think that deep research can accomplish in tens of minutes what would take a human many hours." The model represents a major step forward in OpenAI's work with reasoning systems, building on reinforcement learning techniques introduced in its earlier models. Fulford explained how the company developed the tool: "We launched O1 in September of last year. This was the first model that we released in this new paradigm of training where models are trained to think before answering… and we called this text where the model is thinking, 'chain of thought'." This method of structured, internal reasoning proved effective not only in tasks such as maths and coding, but also in navigating complex real-world information environments. "Around a year ago internally, we were seeing really great success… and we wondered if we could apply these same methods but for tasks that are more similar to what a large number of users do in their daily lives and jobs," Fulford said. Sun detailed how the tool works by combining reasoning with specialised capabilities like web browsing and code execution. "The browser tool helps the model to aggregate or synthesise real-time data, and the Python tool is helping the model to process this data," he explained. The system dynamically alternates between reasoning and action, using reinforcement learning to improve over time. One striking example involved analysing medal data from the 2020 Tokyo Olympics. "You can see how the model interleaved reasoning with actual tool calls to search for information, refine the data, and process it programmatically," Sun said. Unlike older approaches that rely on a single-pass search or instruction-following, deep research iteratively refines its answers. "We train the model with end-to-end reinforcement learning," Sun added. "We directly optimise the model to actively learn from the feedback, both positive and negative." OpenAI tested the model extensively against both public and internal benchmarks. According to Fulford, "the model pairing deep research scored a new high of 26.6%" on the Humanities Last Exam, an expert-level evaluation spanning over 100 subjects. On another benchmark, GAIA, the tool also achieved a state-of-the-art result for multi-step web browsing and reasoning. The model also underwent safety evaluations prior to release. "We did extensive red teaming with external testers, and then also went through preparedness and governance reviews that we always do at OpenAI," Fulford said. Despite strong results, the researchers acknowledged current limitations. "It still may hallucinate facts or infer things incorrectly," Fulford said. "Sometimes it struggles to distinguish between authoritative sources and rumours." Use cases continue to emerge in unexpected domains. "People might be using the model a lot for coding. And that's been a really big use case," Fulford observed. Other domains include scientific and medical research, where professionals have begun verifying the model's output against their own expertise. Users are also adapting their behaviour to suit the model. "We've seen interesting user behaviour where people put a lot of effort into refining their prompts using O1 or another model," Fulford said. "And then only after really refining that instruction, they'll send it to deep research… which makes sense if you're going to wait a long time for an output." Currently, deep research is available to users on the Plus, Pro, Teams, Enterprise and EDU plans. "We're very excited to release a smaller, cheaper model to the free tier," Fulford confirmed. The team also plans to improve personalisation and explore ways to let users incorporate subscription services or private data into the research process. "This showcases how the model can effectively break down a complex task, gather information from various sources, and structure the response coherently for the user," Sun said in closing. OpenAI's forum audience, composed of members across academia, government, and business, left the event with a clear sense that deep research marks a meaningful step toward AI systems capable of handling work currently done by skilled analysts.


Techday NZ
14-05-2025
- Techday NZ
OpenAI forum reveals how deep research transforms inquiry
OpenAI has introduced a new agentic AI system called 'deep research,' designed to handle complex, time-consuming research tasks by simulating the work of a human analyst. Presented by researchers Isa Fulford and Edward Sun during an OpenAI forum event, the new tool is powered by a fine-tuned version of OpenAI's upcoming O3 model and leverages advanced reasoning and browsing capabilities. "Deep research is an agent in ChatGPT that can do work for you independently," Fulford explained. "You give it a prompt, and it will find, analyse, and synthesise hundreds of online sources to create a comprehensive report at the level of a research analyst." The system is intended to help users across a range of sectors—from academia and medicine to business and software development. "Members are finding that deep research asks clarifying questions to refine research before it even starts," said Fulford. "We think that deep research can accomplish in tens of minutes what would take a human many hours." The model represents a major step forward in OpenAI's work with reasoning systems, building on reinforcement learning techniques introduced in its earlier models. Fulford explained how the company developed the tool: "We launched O1 in September of last year. This was the first model that we released in this new paradigm of training where models are trained to think before answering… and we called this text where the model is thinking, 'chain of thought'." This method of structured, internal reasoning proved effective not only in tasks such as maths and coding, but also in navigating complex real-world information environments. "Around a year ago internally, we were seeing really great success… and we wondered if we could apply these same methods but for tasks that are more similar to what a large number of users do in their daily lives and jobs," Fulford said. Sun detailed how the tool works by combining reasoning with specialised capabilities like web browsing and code execution. "The browser tool helps the model to aggregate or synthesise real-time data, and the Python tool is helping the model to process this data," he explained. The system dynamically alternates between reasoning and action, using reinforcement learning to improve over time. One striking example involved analysing medal data from the 2020 Tokyo Olympics. "You can see how the model interleaved reasoning with actual tool calls to search for information, refine the data, and process it programmatically," Sun said. Unlike older approaches that rely on a single-pass search or instruction-following, deep research iteratively refines its answers. "We train the model with end-to-end reinforcement learning," Sun added. "We directly optimise the model to actively learn from the feedback, both positive and negative." OpenAI tested the model extensively against both public and internal benchmarks. According to Fulford, "the model pairing deep research scored a new high of 26.6%" on the Humanities Last Exam, an expert-level evaluation spanning over 100 subjects. On another benchmark, GAIA, the tool also achieved a state-of-the-art result for multi-step web browsing and reasoning. The model also underwent safety evaluations prior to release. "We did extensive red teaming with external testers, and then also went through preparedness and governance reviews that we always do at OpenAI," Fulford said. Despite strong results, the researchers acknowledged current limitations. "It still may hallucinate facts or infer things incorrectly," Fulford said. "Sometimes it struggles to distinguish between authoritative sources and rumours." Use cases continue to emerge in unexpected domains. "People might be using the model a lot for coding. And that's been a really big use case," Fulford observed. Other domains include scientific and medical research, where professionals have begun verifying the model's output against their own expertise. Users are also adapting their behaviour to suit the model. "We've seen interesting user behaviour where people put a lot of effort into refining their prompts using O1 or another model," Fulford said. "And then only after really refining that instruction, they'll send it to deep research… which makes sense if you're going to wait a long time for an output." Currently, deep research is available to users on the Plus, Pro, Teams, Enterprise and EDU plans. "We're very excited to release a smaller, cheaper model to the free tier," Fulford confirmed. The team also plans to improve personalisation and explore ways to let users incorporate subscription services or private data into the research process. "This showcases how the model can effectively break down a complex task, gather information from various sources, and structure the response coherently for the user," Sun said in closing. OpenAI's forum audience, composed of members across academia, government, and business, left the event with a clear sense that deep research marks a meaningful step toward AI systems capable of handling work currently done by skilled analysts.