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Powerful black holes might grow up in bustling galactic neighborhoods

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Powerful black holes might grow up in bustling galactic neighborhoods

A quasar is a galactic object with a supermassive black hole in the center.
International Gemini Observatory/NOIRLab/NSF/AURA/P. Marenfeld, CC BY-NC-SA

Jaclyn Champagne, University of Arizona

As people, we are all shaped by the neighborhoods we grew up in, whether it was a bustling urban center or the quiet countryside. Objects in distant outer are no different.

As an astronomer at the University of Arizona, I like to think of myself as a cosmic historian, tracking how supermassive black holes grew up.

Like you, every supermassive black hole lives in a home – its host galaxy – and a neighborhood – its local group of other galaxies. A supermassive black hole grows by consuming gas already inside its host galaxy, sometimes reaching a times heavier than our Sun.

Theoretical physics predicts that black holes should take billions of years to grow into quasars, which are extra bright and powerful objects powered by black holes. Yet astronomers know that many quasars have formed in only a few hundred million years.

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I'm fascinated by this peculiar problem of faster-than-expected black hole growth and am working to solve it by zooming out and examining the space around these black holes. Maybe the most massive quasars are slickers, forming in hubs of tens or hundreds of other galaxies. Or maybe quasars can grow to huge proportions even in the most desolate regions of the universe.

Galaxy protoclusters

The largest object that can form in the universe is a galaxy cluster, containing hundreds of galaxies pulled by gravity to a common center. Before these grouped galaxies collapse into a single object, astronomers call them protoclusters. In these dense galaxy neighborhoods, astronomers see colliding galaxies, growing black holes and great swarms of gas that will eventually become the next generation of .

These protocluster structures grow much faster than we thought, too, so we have a second cosmic problem to solve – how do quasars and protoclusters evolve so quickly? Are they connected?

Red clouds with a bright white center.
A simulation of a galaxy protocluster forming. In white, clouds of dark matter collapse and merge, while the red shows the motions of gas falling into the gravitational pull of the dark matter halos.
TNG Collaboration, CC BY-NC-SA

To look at protoclusters, astronomers ideally obtain images, which show the galaxy's shape, size and color, and a spectrum, which shows the galaxy's distance from Earth through specific wavelengths of light, for each galaxy in the protocluster.

With telescopes like the James Webb Space Telescope, astronomers can see galaxies and black holes as they were billions of years ago, since the light emitted from distant objects must travel billions of light-years to reach its detectors. We can then look at protoclusters' and quasars' baby pictures to see how they evolved at early times.

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A graph with the y axis reading 'brightness' and the x reading 'wavelength.' A squiggly green line has peaks, with an arrow pointing to the reading 'hydrogen' and 'oxygen.'
An example of a galaxy image and spectrum from the ASPIRE program at the of Arizona. The inset shows the infrared image of a galaxy 800 million years after the Big Bang. The spectrum shows signatures of hydrogen and oxygen emission lines, whose wavelengths translate mathematically to a 3D location in space.
J. Champagne/ASPIRE/University of Arizona

It is only after looking at spectra that astronomers determine whether the galaxies and quasars are actually close together in three-dimensional space. But getting spectra for every galaxy one at a time can take many more hours than any astronomer has, and images can show galaxies that look closer together than they actually are.

So, for a long time, it was only a prediction that massive quasars might be evolving at the centers of vast galactic .

An unprecedented view of quasar environments

Now, Webb has completely revolutionized the search for galaxy neighborhoods because of an instrument called a wide-field slitless spectrograph.

This instrument takes spectra of every galaxy in its field of view simultaneously so astronomers can map out an entire cosmic city at once. It encodes the critical information about galaxies' 3D locations by capturing the light emitted from gas at specific wavelengths – and in only a few hours of observing time.

The first Webb projects are hoping to look at quasar environments focused on a period about 800 million years after the Big Bang. This time period is a sweet spot in which astronomers can view these monster quasars and their neighbors using the light emitted by hydrogen and oxygen. The wavelengths of these light features show where the objects emitting them are along our line of sight, allowing astronomers to complete the census of where galaxies relative to bright quasars.

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One such ongoing is led by the ASPIRE team at the University of Arizona's Steward Observatory. In an early paper, they found a protocluster around an extremely bright quasar and confirmed it with 12 galaxies' spectra.

Another study detected over a hundred galaxies, looking toward the single most luminous quasar known in the early universe. Twenty-four of those galaxies were close to the quasar or in its neighborhood.

Many bright dots representing galaxies, against a black backdrop.
The neighborhood of galaxies around J0305-3150, a quasar identified approximately 800 million years after the Big Bang.
STScI/NASA

In ongoing work, my team is learning more details about mini galaxy cities like these. We want to figure out if individual galaxies show high rates of new star formation, if they contain large masses of old stars or if they are merging with one another. All these metrics would indicate that these galaxies are still actively evolving but had already formed millions of years before we observed them.

Once my team has a list of the properties of the galaxies in an area, we'll compare these properties with a control sample of random galaxies in the universe, far away from any quasar. If these metrics are different enough from the control, we'll have good evidence that quasars do grow up in special neighborhoods – ones developing much faster than the more sparse regions of the universe.

While astronomers still need more than a handful of quasars to prove this hypothesis on a larger scale, Webb has already opened a window into a bright future of discovery in glorious, high-resolution detail.The Conversation

Jaclyn Champagne, JASPER Postdoctoral Researcher, University of Arizona

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‘Dancing’ raisins − a simple kitchen experiment reveals how objects can extract energy from their environment and come to life

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theconversation.com – Saverio Eric Spagnolie, Professor of Mathematics, of Wisconsin-Madison – 2024-05-13 07:29:32

Surface bubble growth can lift objects upward against gravity.

Saverio Spagnolie

Saverio Eric Spagnolie, University of Wisconsin-Madison

Scientific discovery doesn't always require a high-tech laboratory or a hefty budget. Many people have a first-rate lab right in their own homes – their kitchen.

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The kitchen offers plenty of opportunities to view and explore what physicists call soft matter and complex fluids. Everyday phenomena, such as Cheerios clustering in milk or rings left when drops of coffee evaporate, have led to discoveries at the intersection of physics and chemistry and other tasteful collaborations between food scientists and physicists.

Two , Sam Christianson and Carsen Grote, and I published a new study in Nature Communications in May 2024 that dives into another kitchen observation. We studied how objects can levitate in carbonated fluids, a phenomenon that's whimsically referred to as dancing raisins.

The study explored how objects like raisins can rhythmically move up and down in carbonated fluids for several minutes, even up to an hour.

An accompanying Twitter thread about our research went viral, amassing over half a million views in just two days. Why did this particular experiment catch the imaginations of so many?

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Bubbling physics

Sparkling and other carbonated beverages fizz with bubbles because they contain more gas than the fluid can – they're “supersaturated” with gas. When you open a bottle of champagne or a soft drink, the fluid pressure drops and CO₂ molecules begin to make their escape to the surrounding .

Bubbles do not usually form spontaneously in a fluid. A fluid is composed of molecules that like to stick together, so molecules at the fluid boundary are a bit unhappy. This results in surface tension, a force which seeks to reduce the surface area. Since bubbles add surface area, surface tension and fluid pressure normally squeeze any forming bubbles right back out of existence.

But rough patches on a container's surface, like the etchings in some champagne glasses, can protect new bubbles from the crushing effects of surface tension, offering them a to form and grow.

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Bubbles also form inside the microscopic, tubelike cloth fibers left behind after wiping a glass with a towel. The bubbles grow steadily on these tubes and, once they're big enough, detach and float upward, carrying gas out of the container.

But as many champagne enthusiasts who put fruits in their glasses know, surface etchings and little cloth fibers aren't the only places where bubbles can form. Adding a small object like a raisin or a peanut to a sparkling drink also enables bubble growth. These immersed objects act as alluring new surfaces for opportunistic molecules like CO₂ to accumulate and form bubbles.

And once enough bubbles have grown on the object, a levitation act may be performed. Together, the bubbles can lift the object up to the surface of the liquid. Once at the surface, the bubbles pop, dropping the object back down. The then begins again, in a periodic vertical dancing motion.

Dancing raisins

Raisins are particularly good dancers. It takes only a few seconds for enough bubbles to form on a raisin's wrinkly surface before it starts to rise upward – bubbles have a harder time forming on smoother surfaces. When dropped into just-opened sparkling water, a raisin can dance a vigorous tango for 20 minutes, and then a slower waltz for another hour or so.

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Anyone with a few kitchen staples can do their own dancing raisins experiment.

We found that rotation, or spinning, was critically important for coaxing large objects to dance. Bubbles that cling to the bottom of an object can keep it aloft even after the top bubbles pop. But if the object starts to spin even a little bit, the bubbles underneath make the body spin even faster, which results in even more bubbles popping at the surface. And the sooner those bubbles are removed, the sooner the object can get back to its vertical dancing.

Small objects like raisins do not rotate as much as larger objects, but instead they do the twist, rapidly wobbling back and forth.

Modeling the bubbly flamenco

In the paper, we developed a mathematical model to predict how many trips to the surface we would expect an object like a raisin to make. In one experiment, we placed a 3D-printed sphere that acted as a model raisin in a glass of just-opened sparkling water. The sphere traveled from the bottom of the container to the top over 750 times in one hour.

The model incorporated the rate of bubble growth as well as the object's shape, size and surface roughness. It also took into account how quickly the fluid loses carbonation based on the container's geometry, and especially the flow created by all that bubbly activity.

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Small objects covered in bubbles in carbonated water move upwards towards the surface and back down.

Bubble-coated raisins ‘dance' to the surface and plummet once their lifting agents have popped.

Saverio Spagnolie

The mathematical model helped us determine which forces influence the object's dancing the most. For example, the fluid drag on the object turned out to be relatively unimportant, but the ratio of the object's surface area to its volume was critical.

Looking to the future, the model also provides a way to determine some hard to measure quantities using more easily measured ones. For example, just by observing an object's dancing frequency, we can learn a lot about its surface at the microscopic level without to see those details directly.

Different dances in different theaters

These results aren't just interesting for carbonated beverage lovers, though. Supersaturated fluids exist in nature, too – magma is one example.

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As magma in a volcano rises closer to the Earth's surface, it rapidly depressurizes, and dissolved gases from inside the volcano make a dash for the exit, just like the CO₂ in carbonated water. These escaping gases can form into large, high-pressure bubbles and emerge with such force that a volcanic eruption ensues.

The particulate matter in magma may not dance in the same way raisins do in soda water, but tiny objects in the magma may affect how these explosive events play out.

The past decades have also seen an eruption of a different kind – thousands of scientific studies devoted to active matter in fluids. These studies look at things such as swimming microorganisms and the insides of our fluid-filled cells.

Most of these active do not exist in water but instead in more complicated biological fluids that contain the energy necessary to produce activity. Microorganisms absorb nutrients from the fluid around them to continue swimming. Molecular motors carry cargo along a superhighway in our cells by pulling nearby energy in the form of ATP from the environment.

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Studying these systems can help scientists learn more about how the cells and bacteria in the human body function, and how on this planet has evolved to its current state.

Meanwhile, a fluid itself can behave strangely because of a diverse molecular composition and bodies moving around inside it. Many new studies have addressed the behavior of microorganisms in such fluids as mucus, for instance, which behaves like both a viscous fluid and an elastic gel. Scientists still have much to learn about these highly complex systems.

While raisins in soda water seem fairly simple when compared with microorganisms swimming through biological fluids, they offer an accessible way to study generic features in those more challenging settings. In both cases, bodies extract energy from their complex fluid environment while also affecting it, and fascinating behaviors ensue.

New insights about the physical world, from geophysics to biology, will continue to emerge from tabletop-scale experiments – and perhaps from right in the kitchen.The Conversation

Saverio Eric Spagnolie, Professor of Mathematics, University of Wisconsin-Madison

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Why do people hate people?

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theconversation.com – Kristine Hoover, Professor of Organizational Leadership, Gonzaga – 2024-05-13 07:16:27

Biases against certain groups of people can escalate into acts of violence if left unchecked.

Paul Taylor/Stone via Getty Images

Kristine Hoover, Gonzaga University and Yolanda Gallardo, Gonzaga University

Curious Kids is a for children of all ages. If you have a question you'd like an expert to answer, send it to curiouskidsus@theconversation.com.

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Why do people hate people? – Daisy, age 9, Lake Oswego, Oregon


Have you ever said “I hate you” to someone? What about using the “h-word” in casual conversation, like “I hate broccoli”? What are you really feeling when you say that you hate something or someone?

The Merriam-Webster dictionary describes the word “hate” as an “intense hostility and aversion usually deriving from fear, anger, or sense of injury.” All over the world, researchers like us are studying hate from disciplines like education, history, law, leadership, psychology, sociology and many others.

If you had a scary experience with thunderstorms, you might say that you hate thunderstorms. Maybe you have gotten very angry at something that happened at a particular place, so now you say you hate going there. Maybe someone said something hurtful to you, so you say you hate that person.

Understanding hate as an emotional response can you recognize your feelings about something or someone and be curious about where those feelings are coming from. This awareness will give you time to gather more information and imagine the other person's perspective.

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So what is hate and why do people hate? There are many answers to these questions.

What hate isn't

Hate, according to the U.S. Department of Justice, “does not mean rage, anger or general dislike.”

Sometimes people think they have to feel or believe a certain way about another person or group of people because of what they hear or see around them. For example, people might say they hate another person or group of people when what they really mean is that they don't agree with them, don't understand them or don't like how they behave or the things they believe in.

View between the arm of a person with their hands on their hips, focusing on a child sitting at table with a glare

Do you hate this person, or are you angry, or afraid?

Lourdes Balduque/Moment via Getty Images

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It is easy to blame others for things you don't believe or experiences you don't like. Think about times you might have heard someone at school say they hate a classmate or a teacher. Could they have been angry, hurt or confused about something but used the word hate to explain or name how they were feeling?

When you don't understand someone else, it can make you nervous and even afraid. Instead of being curious about each other's unique experiences, people may judge others for being different – they may have a different skin color, practice a different religion, come from a different country, be older or younger, or use a wheelchair.

When people judge people as being less important or less human than themselves, that is a form of hatred.

What hate is

The U.S. Department of Justice defines hate as “bias against people or groups with specific characteristics that are defined by the law.” These characteristics can include a person's race, religion, gender, sexual orientation, disability and national origin.

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One way to think about hate is as a pyramid. At the bottom of the pyramid, hate is a feeling that grows from biased attitudes about others, like stereotypes that certain groups of people are animals, lazy or stupid.

Sometimes these biased attitudes and feelings provide a foundation for people to act out their biases, such as through bullying, exclusion or insults. For example, many Asian people in the U.S. experienced an increase in hate incidents during the pandemic. If communities accept biases as OK, some people may move up the pyramid and think it is also OK to discriminate, or believe that specific groups of people are not welcome in certain neighborhoods or because of who they are.

Near the top of the pyramid, some people commit violence or hate crimes because they believe their own way of being is better than others'. They may threaten or physically harm others, or destroy property. At the very top of the pyramid is genocide, the intent to destroy a particular group – like what Jewish people experienced during World War II or what Rohingya people are experiencing in Myanmar, near China.

Hate at the middle and higher levels of the pyramid happens because no one took action to discourage the biased feelings, attitudes and actions at the lower levels of the pyramid.

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Taking action against hate

Not only can individual people hate, there are also hate groups like the Ku Klux Klan that attack people who are not white, straight or Christian. Sometimes hate has been written into law like the Indian Removal Act or Jim Crow laws that persecuted Native and Black Americans. If we stay silent when we encounter hate, that hatred can grow and do greater levels of harm.

There are many ways you can help stop hate in your everyday .

Pay attention to what is being said around you. If the people you spend a lot of time with are saying hateful things about other groups, consider speaking up or changing who you hang out with and where. Be an upstander – sit with someone who is being targeted and report when you see or hear hate incidents.

Close-up of group of protestors yelling with their fists in the air

Protests are one way people speak up on behalf of a specific group.

FG Trade/E+ via Getty Images

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Start noticing when you are letting hateful words or behaviors into your thoughts and actions. Get to know what hate looks and sounds like in yourself and in others, what you see online.

Be open to meeting others who have different experiences than you and give them a chance to let you know who they are. Be brave and face your fears. Be curious and kind.

You are not alone in standing up to hate. Many human rights groups and government initiatives are doing the work of eradicating hate, too. We all have a “response-ability,” or the ability to respond. As civil rights leader the Rev. Martin Luther King Jr. said, “Darkness cannot out darkness, only light can do that. Hate cannot drive out hate, only love can do that.”

You just might find that it is easier to love other people than to hate them. Others will see how you behave and will follow your lead.

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Hello, curious kids! Do you have a question you'd like an expert to answer? Ask an adult to send your question to CuriousKidsUS@theconversation.com. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you're wondering, too. We won't be able to answer every question, but we will do our best.The Conversation

Kristine Hoover, Professor of Organizational Leadership, Gonzaga University and Yolanda Gallardo, Dean of Education, Gonzaga University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Algorithms help people see and correct their biases, study shows

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theconversation.com – Carey K. Morewedge, Professor of Marketing and Everett W. Lord Distinguished Faculty Scholar, Boston – 2024-05-10 07:27:14

Algorithms could serve as mirrors for you to check your biases.

FG Trade/E+ via Getty Images

Carey K. Morewedge, Boston University

Algorithms are a staple of modern . People rely on algorithmic recommendations to wade through deep catalogs and find the best movies, routes, information, products, people and investments. Because people train algorithms on their decisions – for example, algorithms that make recommendations on e-commerce and social sites – algorithms learn and codify human biases.

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Algorithmic recommendations exhibit bias toward popular choices and information that evokes outrage, such as partisan news. At a societal level, algorithmic biases perpetuate and amplify structural racial bias in the judicial system, gender bias in the people companies hire, and wealth inequality in urban development.

Algorithmic bias can also be used to reduce human bias. Algorithms can reveal hidden structural biases in . In a paper published in the Proceedings of the National Academy of Science, my colleagues and I found that algorithmic bias can people better recognize and correct biases in themselves.

The bias in the mirror

In nine experiments, Begum Celikitutan, Romain Cadario and I had research participants rate Uber drivers or Airbnb listings on their driving skill, trustworthiness or the likelihood that they would rent the listing. We gave participants relevant details, like the number of trips they'd driven, a description of the property, or a star rating. We also included an irrelevant biasing piece of information: a photograph revealed the age, gender and attractiveness of drivers, or a name that implied that listing hosts were white or Black.

After participants made their ratings, we showed them one of two ratings summaries: one showing their own ratings, or one showing the ratings of an algorithm that was trained on their ratings. We told participants about the biasing feature that might have influenced these ratings; for example, that Airbnb guests are less likely to rent from hosts with distinctly African American names. We then asked them to judge how much influence the bias had on the ratings in the summaries.

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The author how algorithms can be useful as a mirror of people's biases.

Whether participants assessed the biasing influence of race, age, gender or attractiveness, they saw more bias in ratings made by algorithms than themselves. This algorithmic mirror effect held whether participants judged the ratings of real algorithms or we showed participants their own ratings and deceptively told them that an algorithm made those ratings.

Participants saw more bias in the decisions of algorithms than in their own decisions, even when we gave participants a cash bonus if their bias judgments matched the judgments made by a different participant who saw the same decisions. The algorithmic mirror effect held even if participants were in the marginalized category – for example, by identifying as a woman or as Black.

Research participants were as able to see biases in algorithms trained on their own decisions as they were able to see biases in the decisions of other people. Also, participants were more likely to see the influence of racial bias in the decisions of algorithms than in their own decisions, but they were equally likely to see the influence of defensible features, like star ratings, on the decisions of algorithms and on their own decisions.

Bias blind spot

People see more of their biases in algorithms because the algorithms remove people's bias blind spots. It is easier to see biases in others' decisions than in your own because you use different evidence to evaluate them.

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When examining your decisions for bias, you search for evidence of conscious bias – whether you thought about race, gender, age, status or other unwarranted features when deciding. You overlook and excuse bias in your decisions because you lack access to the associative machinery that drives your intuitive judgments, where bias often plays out. You might think, “I didn't think of their race or gender when I hired them. I hired them on merit alone.”

The bias blind spot explained.

When examining others' decisions for bias, you lack access to the processes they used to make the decisions. So you examine their decisions for bias, where bias is evident and harder to excuse. You might see, for example, that they only hired white .

Algorithms remove the bias blind spot because you see algorithms more like you see other people than yourself. The -making processes of algorithms are a black box, similar to how other people's are inaccessible to you.

Participants in our study who were most likely to demonstrate the bias blind spot were most likely to see more bias in the decisions of algorithms than in their own decisions.

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People also externalize bias in algorithms. Seeing bias in algorithms is less threatening than seeing bias in yourself, even when algorithms are trained on your choices. People put the blame on algorithms. Algorithms are trained on human decisions, yet people call the reflected bias “algorithmic bias.”

Corrective lens

Our experiments show that people are also more likely to correct their biases when they are reflected in algorithms. In a final experiment, we gave participants a to correct the ratings they evaluated. We showed each participant their own ratings, which we attributed either to the participant or to an algorithm trained on their decisions.

Participants were more likely to correct the ratings when they were attributed to an algorithm because they believed the ratings were more biased. As a result, the final corrected ratings were less biased when they were attributed to an algorithm.

Algorithmic biases that have pernicious effects have been well documented. Our findings show that algorithmic bias can be leveraged for good. The first step to correct bias is to recognize its influence and direction. As mirrors revealing our biases, algorithms may improve our decision-making.The Conversation

Carey K. Morewedge, Professor of Marketing and Everett W. Lord Distinguished Faculty Scholar, Boston University

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