Science
Jets from powerful black holes can point astronomers toward where − and where not − to look for life in the universe
David Garofalo, Kennesaw State University
One of the most powerful objects in the universe is a radio quasar – a spinning black hole spraying out highly energetic particles. Come too close to one, and you’d get sucked in by its gravitational pull, or burn up from the intense heat surrounding it. But ironically, studying black holes and their jets can give researchers insight into where potentially habitable worlds might be in the universe.
As an astrophysicist, I’ve spent two decades modeling how black holes spin, how that creates jets, and how they affect the environment of space around them.
What are black holes?
Black holes are massive, astrophysical objects that use gravity to pull surrounding objects into them. Active black holes have a pancake-shaped structure around them called an accretion disk, which contains hot, electrically charged gas.
The plasma that makes up the accretion disk comes from farther out in the galaxy. When two galaxies collide and merge, gas is funneled into the central region of that merger. Some of that gas ends up getting close to the newly merged black hole and forms the accretion disk.
There is one supermassive black hole at the heart of every massive galaxy.
Black holes and their disks can rotate, and when they do, they drag space and time with them – a concept that’s mind-boggling and very hard to grasp conceptually. But black holes are important to study because they produce enormous amounts of energy that can influence galaxies.
How energetic a black hole is depends on different factors, such as the mass of the black hole, whether it rotates rapidly, and whether lots of material falls onto it. Mergers fuel the most energetic black holes, but not all black holes are fed by gas from a merger. In spiral galaxies, for example, less gas tends to fall into the center, and the central black hole tends to have less energy.
One of the ways they generate energy is through what scientists call “jets” of highly energetic particles. A black hole can pull in magnetic fields and energetic particles surrounding it, and then as the black hole rotates, the magnetic fields twist into a jet that sprays out highly energetic particles.
Magnetic fields twist around the black hole as it rotates to store energy – kind of like when you pull and twist a rubber band. When you release the rubber band, it snaps forward. Similarly, the magnetic fields release their energy by producing these jets.
These jets can speed up or suppress the formation of stars in a galaxy, depending on how the energy is released into the black hole’s host galaxy.
Rotating black holes
Some black holes, however, rotate in a different direction than the accretion disk around them. This phenomenon is called counterrotation, and some studies my colleagues and I have conducted suggest that it’s a key feature governing the behavior of one of the most powerful kinds of objects in the universe: the radio quasar.
Radio quasars are the subclass of black holes that produce the most powerful energy and jets.
You can imagine the black hole as a rotating sphere, and the accretion disk as a disk with a hole in the center. The black hole sits in that center hole and rotates one way, while the accretion disk rotates the other way.
This counterrotation forces the black hole to spin down and eventually up again in the other direction, called corotation. Imagine a basketball that spins one way, but you keep tapping it to rotate in the other. The tapping will spin the basketball down. If you continue to tap in the opposite direction, it will eventually spin up and rotate in the other direction. The accretion disk does the same thing.
Since the jets tap into the black hole’s rotational energy, they are powerful only when the black hole is spinning rapidly. The change from counterrotation to corotation takes at least 100 million years. Many initially counterrotating black holes take billions of years to become rapidly spinning corotating black holes.
So, these black holes would produce powerful jets both early and later in their lifetimes, with an interlude in the middle where the jets are either weak or nonexistent.
When the black hole spins in counterrotation with respect to its accretion disk, that motion produces strong jets that push molecules in the surrounding gas close together, which leads to the formation of stars.
But later, in corotation, the jet tilts. This tilt makes it so that the jet impinges directly on the gas, heating it up and inhibiting star formation. In addition to that, the jet also sprays X-rays across the galaxy. Cosmic X-rays are bad for life because they can harm organic tissue.
For life to thrive, it most likely needs a planet with a habitable ecosystem, and clouds of hot gas saturated with X-rays don’t contain such planets. So, astronomers can instead look for galaxies without a tilted jet coming from its black hole. This idea is key to understanding where intelligence could potentially have emerged and matured in the universe.
Black holes as a guide
By early 2022, I had built a black hole model to use as a guide. It could point out environments with the right kind of black holes to produce the greatest number of planets without spraying them with X-rays. Life in such environments could emerge to its full potential. https://www.youtube.com/embed/b7mTVX9IE0s?wmode=transparent&start=0 Looking at black holes and their role in star formation could help scientists predict when and where life was most likely to form.
Where are such conditions present? The answer is low-density environments where galaxies had merged about 11 billion years ago.
These environments had black holes whose powerful jets enhanced the rate of star formation, but they never experienced a bout of tilted jets in corotation. In short, my model suggested that theoretically, the most advanced extraterrestrial civilization would have likely emerged on the cosmic scene far away and billions of years ago.
David Garofalo, Professor of Physics, Kennesaw State University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/
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Last Updated on February 10, 2026 by Daily News Staff
Valerie Thomas is a true pioneer in the world of science and technology. A NASA engineer and physicist, she is best known for inventing the illusion transmitter, a groundbreaking device that creates 3D images using concave mirrors. This invention laid the foundation for modern 3D imaging and virtual reality technologies.
Beyond her inventions, Thomas broke barriers as an African American woman in STEM, mentoring countless young scientists and advocating for diversity in science and engineering. Her work at NASA’s Goddard Space Flight Center helped advance satellite technology and data visualization, making her contributions both innovative and enduring.
In our latest short video, we highlight Valerie Thomas’ remarkable journey—from her early passion for science to her groundbreaking work at NASA. Watch and be inspired by a true STEM pioneer whose legacy continues to shape the future of space and technology.
🎥 Watch the video here: https://youtu.be/P5XTgpcAoHw
Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter. https://stmdailynews.com/the-knowledge/
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Forgotten Genius Fridays is a weekly collection of short videos and articles dedicated to inventors, innovators, scientists, and creators whose impact changed the world—but whose names were often left out of the textbooks.
From life-saving inventions and cultural breakthroughs to game-changing ideas buried by bias, our series digs up the truth behind the minds that mattered.
Each episode of The Knowledge runs 30–90 seconds, designed for curious minds on the go—perfect for YouTube Shorts, TikTok, Reels, and quick reads.
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Beneath the Waves: The Global Push to Build Undersea Railways
Undersea railways are transforming transportation, turning oceans from barriers into gateways. Proven by tunnels like the Channel and Seikan, these innovations offer cleaner, reliable connections for passengers and freight. Ongoing projects in China and Europe, alongside future proposals, signal a new era of global mobility beneath the waves.
For most of modern history, oceans have acted as natural barriers—dividing nations, slowing trade, and shaping how cities grow. But beneath the waves, a quiet transportation revolution is underway. Infrastructure once limited by geography is now being reimagined through undersea railways.
Undersea rail tunnels—like the Channel Tunnel and Japan’s Seikan Tunnel—proved decades ago that trains could reliably travel beneath the ocean floor. Today, new projects are expanding that vision even further.
Around the world, engineers and governments are investing in undersea railways—tunnels that allow high-speed trains to travel beneath oceans and seas. Once considered science fiction, these projects are now operational, under construction, or actively being planned.
Undersea Rail Is Already a Reality
Japan’s Seikan Tunnel and the Channel Tunnel between the United Kingdom and France proved decades ago that undersea railways are not only possible, but reliable. These tunnels carry passengers and freight beneath the sea every day, reshaping regional connectivity.
Undersea railways are cleaner than short-haul flights, more resilient than bridges, and capable of lasting more than a century. As climate pressures and congestion increase, rail beneath the sea is emerging as a practical solution for future mobility.
What’s Being Built Right Now
China is currently constructing the Jintang Undersea Railway Tunnel as part of the Ningbo–Zhoushan high-speed rail line, while Europe’s Fehmarnbelt Fixed Link will soon connect Denmark and Germany beneath the Baltic Sea. These projects highlight how transportation and technology are converging to solve modern mobility challenges.
The Mega-Projects Still on the Drawing Board
Looking ahead, proposals such as the Helsinki–Tallinn Tunnel and the long-studied Strait of Gibraltar rail tunnel could reshape global affairs by linking regions—and even continents—once separated by water.
Why Undersea Rail Matters
The future of transportation may not rise above the ocean—but run quietly beneath it.
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Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain
With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
Seth King, University of Iowa
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.
Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.
Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.
As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.
Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.
A faster IEP, but how individualized?
AI is already shaping special education planning, personnel preparation and assessment.
One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.
But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.
Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.
Training and diagnosing disabilities
There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.
Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.
Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.
Privacy and trust concerns
There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.
The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students’ data privacy and the rights of parents to access and hold their children’s data.
What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.
These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.
But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.
Embedded bias
At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.
Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.
Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.
By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.
Filling the gap
Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.
The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.
But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.
Seth King, Associate Profess of Special Education, University of Iowa
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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