Sun Releases Strong Flare - UNIVERSE

The Sun emitted a strong flare, peaking at 5:49 p.m. ET on Tuesday, June 17, 2025. NASA’s Solar Dynamics Observatory, which watches the Sun constantly, captured an image of the event.

NASA’s Solar Dynamics Observatory captured this image of a solar flare — seen as the bright flash near the middle of the image — on June 17, 2025. The image shows a subset of extreme ultraviolet light that highlights the extremely hot material in flares and which is colorized in teal.

NASA/SDO

Solar flares are powerful bursts of energy. Flares and solar eruptions can impact radio communications, electric power grids, navigation signals, and pose risks to spacecraft and astronauts.    

This flare is classified as an X1.2 flare. X-class denotes the most intense flares, while the number provides more information about its strength.

To see how such space weather may affect Earth, please visit NOAA’s Space Weather Prediction Center https://spaceweather.gov/, the U.S. government’s official source for space weather forecasts, watches, warnings, and alerts. NASA works as a research arm of the nation’s space weather effort. NASA observes the Sun and our space environment constantly with a fleet of spacecraft that study everything from the Sun’s activity to the solar atmosphere, and to the particles and magnetic fields in the space surrounding Earth. 

Source: Sun Releases Strong Flare - NASA Science  

Passive cooling paint sweats off heat to deliver 10X cooling and 30% energy savings - Engineering - Energy & Green Tech - techxplore

Comparison of radiative cooling paint versus integrated cooling paint for buildings. Credit: Science (2025). DOI: https://doi.org/10.1126/science.adt3372

A new cement-based paint can cool down the building by sweating off the heat. The cooling paint, named CCP-30, was designed by an international team of researchers and features a nanoparticle-modified porous structure composed of a calcium silicate hydrate (C-S-H) gel network.

This design enabled it to achieve superior cooling by combining both radiative, evaporative and reflective cooling mechanisms, which allowed it to reflect 88–92% of sunlight, emit 95% of the heat as infrared radiation, and hold about 30% of its weight in water, making it a paint ideal for keeping spaces cool throughout the day and across seasons.

As per the findings published in Science, the paint provides 10 times the cooling power of commercial cooling paints in tropical climates, resulting in electricity savings of 30 to 40%.

Space cooling systems take up nearly 20% of the total electricity usage in buildings around the world today, making them a significant contributor to global warming due to high CO₂ emissions. It also plays an important role in driving the urban heat island (UHI) effect, a phenomenon where city centers experience much higher air temperatures than the surrounding suburban areas. Passive cooling strategies have emerged as an energy-efficient and sustainable approach to reducing emissions and urban heat island (UHI) effects.

Short Film: https://techxplore.com/news/2025-06-passive-cooling-10x-energy.html

Negligible optical contrast and mechanical swelling of CCP-30 upon wetting. Credit: Science (2025). DOI: 10.1126/science.adt3372

Most passive cooling paints rely on a radiative mechanism, which leverages the passive cooling process where objects on Earth lose heat to the colder outer space by emitting infrared radiation. This process works wonders in dry climates and under clear skies, but suffers in humid regions with frequent cloud cover. The highly directional nature of this process makes it far less effective on vertical surfaces that aren't under direct line of sight to the sky.

The researchers were able to overcome these major pain points of the radiative mechanism by harnessing the power of evaporative cooling. By taking advantage of water's high latent heat (~2256 J/g), evaporative cooling absorbed a significant amount of thermal energy from a surface as the water turns from liquid to vapor. It also provided non-directional cooling, wherein the mechanism was not dependent on factors like surface orientation and restrictive side views.

As a result, the newly designed paint delivered powerful cooling performance, boasting an impressive 95% infrared emittance even under direct sunlight, along with 88–92% solar reflectance in both wet and dry conditions. 

Fluidic property and cooling mechanism. Credit: Science (2025). DOI: https://doi.org/10.1126/science.adt3372

What truly set CCP-30 paint apart was its self-replenishing ability—absorbing water from rain and atmospheric moisture to sustain evaporative cooling over time—without compromising how the paint interacts with light when wet.

In field tests conducted in tropical Singapore, CCP-30 outperformed commercial white paints with its exceptional cooling power. Pilot-scale building tests showed 30–40% electricity savings, while life-cycle analysis revealed a 28% lower carbon footprint per functional unit compared to standard white paint.

Apart from offering a practical and long-term solution for mitigating the urban heat island effect, the paint presented itself as an innovative solution with real-world potential to support global decarbonization efforts. 

by Sanjukta Mondal, Phys.org

edited by Gaby Clark, reviewed by Robert Egan 

Source: Passive cooling paint sweats off heat to deliver 10X cooling and 30% energy savings

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Frigid Exoplanet in Strange Orbit Imaged by NASA’s Webb - UNIVERSE

A planetary system described as abnormal, chaotic, and strange by researchers has come into clearer view with NASA’s James Webb Space Telescope. Using Webb’s NIRCam (Near-Infrared Camera), researchers have successfully imaged one of two known planets surrounding the star 14 Herculis, located 60 light-years away from Earth in our own Milky Way galaxy.

The exoplanet, 14 Herculis c, is one of the coldest imaged to date. While there are nearly 6,000 exoplanets that have been discovered, only a small number of those have been directly imaged, most of those being very hot (think hundreds or even thousands of degrees Fahrenheit). The new data suggests 14 Herculis c, which weighs about 7 times the planet Jupiter, is as cool as 26 degrees Fahrenheit (minus 3 degrees Celsius).

Image: 14 Herculis c (NIRCam)

This image of exoplanet 14 Herculis c was taken by NASA’s James Webb Space Telescope’s NIRCam (Near-Infrared Camera). A star symbol marks the location of the host star 14 Herculis, whose light has been blocked by a coronagraph on NIRCam (shown here as a dark circle outlined in white).

NASA, ESA, CSA, STScI, W. Balmer (JHU), D. Bardalez Gagliuffi (Amherst College)

The team’s results covering 14 Herculis c have been accepted for publication in The Astrophysical Journal Letters and were presented in a press conference Tuesday at the 246th meeting of the American Astronomical Society in Anchorage, Alaska.

“The colder an exoplanet, the harder it is to image, so this is a totally new regime of study that Webb has unlocked with its extreme sensitivity in the infrared,” said William Balmer, co-first author of the new paper and graduate student at Johns Hopkins University. “We are now able to add to the catalog of not just hot, young exoplanets imaged, but older exoplanets that are far colder than we’ve directly seen before Webb.”

Webb’s image of 14 Herculis c also provides insights into a planetary system unlike most others studied in detail with Webb and other ground- and space-based `observatories. The central star, 14 Herculis, is almost Sun-like – it is similar in age and temperature to our own Sun, but a little less massive and cooler.

There are two planets in this system – 14 Herculis b is closer to the star, and covered by the coronagraphic mask in the Webb image. These planets don’t orbit the host star on the same plane like our solar system. Instead, they cross each other like an ‘X’, with the star being at the center. That is, the orbital planes of the two planets are inclined relative to one another at an angle of about 40 degrees. The planets tug and pull at one another as they orbit the star.

This is the first time an image has ever been snapped of an exoplanet in such a mis-aligned system.

Scientists are working on several theories for just how the planets in this system got so “off track.” One of the leading concepts is that the planets scattered after a third planet was violently ejected from the system early in its formation.

“The early evolution of our own solar system was dominated by the movement and pull of our own gas giants,” added Balmer. “They threw around asteroids and rearranged other planets. Here, we are seeing the aftermath of a more violent planetary crime scene. It reminds us that something similar could have happened to our own solar system, and that the outcomes for small planets like Earth are often dictated by much larger forces.”

Understanding the Planet’s Characteristics With Webb

Webb’s new data is giving researchers further insights into not just the temperature of 14 Herculis c, but other details about the planet’s orbit and atmosphere.

Findings indicate the planet orbits around 1.4 billion miles from the host star in a highly elliptical, or football-shaped orbit, closer in than previous estimates. This is around 15 times farther from the Sun than Earth. On average, this would put 14 Herculis c between Saturn and Uranus in our solar system.

The planet’s brightness at 4.4 microns measured using Webb’s coronagraph, combined with the known mass of the planet and age of the system, hints at some complex atmospheric dynamics at play.

“If a planet of a certain mass formed 4 billion years ago, then cooled over time because it doesn't have a source of energy keeping it warm, we can predict how hot it should be today,” said Daniella C. Bardalez Gagliuffi of Amherst College, co-first author on the paper with Balmer. “Added information, like the perceived brightness in direct imaging, would in theory support this estimate of the planet’s temperature.”

However, what researchers expect isn’t always reflected in the results. With 14 Herculis c, the brightness at this wavelength is fainter than expected for an object of this mass and age. The research team can explain this discrepancy, though. It’s called carbon disequilibrium chemistry, something often seen in brown dwarfs.

“This exoplanet is so cold, the best comparisons we have that are well-studied are the coldest brown dwarfs,” Bardalez Gagliuffi explained. “In those objects, like with 14 Herculis c, we see carbon dioxide and carbon monoxide existing at temperatures where we should see methane. This is explained by churning in the atmosphere. Molecules made at warmer temperatures in the lower atmosphere are brought to the cold, upper atmosphere very quickly.”

Researchers hope Webb’s image of 14 Herculis c is just the beginning of a new phase of investigation into this strange system.

While the small dot of light obtained by Webb contains a plethora of information, future spectroscopic studies of 14 Herculis could better constrain the atmospheric properties of this interesting planet and help researchers understand the dynamics and formation pathways of the system.

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).

To learn more about Webb, visit: https://science.nasa.gov/webb 

Source: Frigid Exoplanet in Strange Orbit Imaged by NASA’s Webb - NASA Science

Benchmarking hallucinations: New metric tracks where multimodal reasoning models go wrong - Computer Sciences - Machine learning & AI - techxplore

(a) Example of outputs from a reasoning model and a non-reasoning model on a perception task. Red highlights indicate visual hallucination. Multimodal reasoning models are generally more prone to amplifying hallucinations during the reasoning process compared to their non-reasoning counterparts. (b) Performance of different models on reasoning and perception tasks in the RH-Bench dataset. Better performing models are positioned in the upper right corner. Baseline non-reasoning models of varying scales typically exhibit weaker reasoning capabilities and fewer hallucination, whereas reasoning models display the opposite trend. Credit: Liu et al.

Over the past decades, computer scientists have introduced increasingly sophisticated machine learning-based models, which can perform remarkably well on various tasks. These include multimodal large language models (MLLMs), systems that can process and generate different types of data, predominantly texts, images and videos.

Some of these models, such as OpenAI's GPT4 with Vision (GPT-4V), DeepSeek-R1 and Google Gemini, are now widely used by users worldwide to create specific multi-modal content, including images for social media posts or articles, as well as texts tailored for specific uses.

While the reasoning abilities of these models have improved considerably in recent years, allowing them to solve mathematical and reasoning problems, studies showed that they sometimes respond to things that are not grounded in the input data, for instance, by describing details that do not actually exist in an input image.

These hallucinations have been linked to language priors and internal biases that a model may have acquired during training while it was analyzing large text datasets. These biases can override the visual information fed to the model (i.e., input images), causing the model to incorrectly complete the tasks assigned to it.

Researchers at UC Santa Cruz, Stanford University and UC Santa Barbara have recently developed a metric and a diagnostic benchmark that could help to study these hallucinations, specifically focusing on the relationship between the reasoning of MLLMs and their tendency to hallucinate when asked to describe what is portrayed in an input image. These new research tools, presented in a paper on the arXiv preprint server, could contribute to the assessment and advancement of MLLMs.

"Test-time compute has empowered multimodal large language models to generate extended reasoning chains, yielding strong performance on tasks such as multimodal math reasoning," wrote Chengzhi Liu, Zhongxing Xu and their colleagues in their paper.

"However, this improved reasoning ability often comes with increased hallucination: as generations become longer, models tend to drift away from image-grounded content and rely more heavily on language priors." 

Comparison of reasoning and non-reasoning models on five perception benchmarks. Results are shown for 3B models (left) and 7B models (right). Higher scores indicate lower hallucination. Credit: arXiv (2025). DOI: 10.48550/arxiv.2505.21523

The researchers first assessed the performance of MLLMs on complex reasoning tasks and found that as reasoning chains (i.e., sequences of logical steps required to solve a problem) grew in length, the models' tendency to hallucinate also increased. They suggested that these hallucinations emerged due to reduced attention to visual stimuli and a greater reliance on language priors.

"Attention analysis shows that longer reasoning chains lead to reduced focus on visual inputs, which contributes to hallucination," wrote Liu, Xu and their colleagues.

"To systematically study this phenomenon, we introduce RH-AUC, a metric that quantifies how a model's perception accuracy changes with reasoning length, allowing us to evaluate whether the model preserves visual grounding during reasoning. We also release RH-Bench, a diagnostic benchmark that spans a variety of multimodal tasks, designed to assess the trade-off between reasoning ability and hallucination."

RH-AUC and RH-Bench, the metrics and benchmarks developed by Liu, Xu and his colleagues, could soon be used by other researchers to evaluate the interplay between the reasoning abilities of specific MLLMs and the risk of hallucinating. Moreover, the observations presented in the team's paper could guide future efforts aimed at developing models that can reliably tackle complex reasoning tasks without becoming prone to hallucinations.

"Our analysis reveals that larger models typically achieve a better balance between reasoning and perception and that this balance is influenced more by the types and domains of training data than by its overall volume," wrote Liu, Xu and their colleagues. "These findings underscore the importance of evaluation frameworks that jointly consider both reasoning quality and perceptual fidelity."

Written for you by our author Ingrid Fadelli, edited by Gaby Clark, and fact-checked and reviewed by Robert Egan—this article is the result of careful human work. We rely on readers like you to keep independent science journalism alive. If this reporting matters to you, please consider a donation (especially monthly). You'll get an ad-free account as a thank-you. 

by Ingrid Fadelli, Phys.org

edited by Gaby Clark, reviewed by Robert Egan 

Source: Benchmarking hallucinations: New metric tracks where multimodal reasoning models go wrong  

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