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What makes black holes grow and new stars form? Machine learning helps solve the mystery

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What makes black holes grow and new stars form? Machine learning helps solve the mystery


When they are active, supermassive black holes play a crucial role in the way galaxies evolve. Until now, growth was thought to be triggered by the violent collision of two galaxies followed by their merger, however new research led by the University of Bath suggests galaxy mergers alone are not enough to fuel a black hole — a reservoir of cold gas at the centre the host galaxy is needed too.

The new study, published this week in the journal Monthly Notices of the Royal Astronomical Society is believed to be the first to use machine learning to classify galaxy mergers with the specific aim of exploring the relationship between galaxy mergers, supermassive black-hole accretion and star formation. Until now, mergers were classified (often incorrectly) through human observation alone.

“When humans look for galaxy mergers, they don’t always know what they are looking at and they use a lot of intuition to decide if a merger has happened,” said Mathilda Avirett-Mackenzie, PhD student in the Department of Physics at the University of Bath and first author on the research paper. The study was a collaboration between partners from BiD4BEST (Big Data Applications for Black Hole Evolution Studies), whose Innovative Training Network provides doctorial training in the formation of supermassive black holes.

She added: “By training a machine to classify mergers, you get a much more truthful reading of what galaxies are actually doing.”

Supermassive black holes

Supermassive black holes are found in the centre of all massive galaxies (to give a sense of scale, the Milky Way, with around 200 billion stars, is only a medium-sized galaxy). These supersized black holes typically weigh between millions and billions of times the mass of our sun.

Through most of their lives, these black holes are quiescent, sitting quietly while matter orbits around them, and having little impact on the galaxy as a whole. But for brief phases in their lives (brief only on an astronomical scale, and most likely lasting millions to hundreds of millions of years), they use gravitation forces to draw large amounts of gas towards them (an event known as accretion), resulting in a bright disk that can outshine the entire galaxy.

It’s these short phases of activity that are most important for galaxy evolution, as the massive amounts of energy released through accretion can impact how stars form in galaxies. For good reason then, establishing what causes a galaxy to move between its two states — quiescent and star-forming — is one of the greatest challenges in astrophysics.

“Determining the role of supermassive black holes in galaxy evolution is crucial in our studies of the universe,” said Ms Avirett-Mackenzie.

Human inspection vs machine learning

For decades, theoretical models have suggested black holes grow when galaxies merge. However, astrophysicists studying the connection between galaxy mergers and black-hole growth over many years have been challenging these models with a simple question: How do we reliably identify mergers of galaxies?

Visual inspection has been the most commonly used method. Human classifiers — either experts or members of the public — observe galaxies and identify high asymmetries or long tidal tails (thin, elongated regions of stars and interstellar gas that extend into space), both of which are associated with galaxy mergers.

However, this observational method is both time-consuming and unreliable, as it’s easy for humans to make mistakes in their classifications. As a result, merger studies often yield contradictory results.

For the new Bath-led study, the researchers set themselves the challenge of improving the way mergers are classified by studying the connection between black-hole growth and galaxy evolution through the use of artificial intelligence.

Inspired by the human brain

They trained a neural network (a subset of machine learning inspired by the human brain and mimicking the way biological neurons signal to one another) on simulated galaxy mergers, then applied this model to galaxies observed in the cosmos.

By doing so, they were able to identify mergers without human biases and study the connection between galaxy mergers and black-hole growth. They showed that the neural network outperforms human classifiers in identifying mergers, and in fact, human classifiers tend to mistake regular galaxies for mergers.

Applying this new methodology, the researchers were able to show that mergers are not strongly associated with black-hole growth. Merger signatures are equally common in galaxies with and without accreting supermassive black holes.

Using an extremely large sample of approximately 8,000 accreting black-hole systems — which allowed the team to study the question in much more detail — it was found that mergers led to black-hole growth only in a very specific type of galaxies: star-forming galaxies containing significant amounts of cold gas.

This shows that galaxy mergers alone are not enough to fuel black holes: large amounts of cold gas must also be present to allow the black hole to grow.

Ms Avirett-Mackenzie said: “For galaxies to form stars, they must contain cold gas clouds that are able to collapse into stars. Highly energetic processes like supermassive black-hole accretion heats this gas up, either rendering it too energetic to collapse or blowing it out of the galaxy.”

She added: “On a clear night, you can just about spot this process happening in real time with the Orion Nebula — a large, star-forming region in our galaxy and the closest of its kind to Earth — where you can see some stars that were formed recently and others that are still forming.”

Dr Carolin Villforth, senior lecturer in the Department of Physics and Ms Avirett-Mackenzie’s supervisor at Bath, said: “Until now, everyone was studying mergers the same way — through visual classification. With this method, when using expert classifiers that can spot more subtle features, we were only able to look at a couple of hundred galaxies, no more.

“Using machine learning instead opens up an entirely new and very exciting field where you can analyse thousands of galaxies at a time. You get consistent results over really large samples, and at any given moment, you can look at many different properties of a black hole.”



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Early dark energy could resolve cosmology’s two biggest puzzles

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What makes black holes grow and new stars form? Machine learning helps solve the mystery


A new study by MIT physicists proposes that a mysterious force known as early dark energy could solve two of the biggest puzzles in cosmology and fill in some major gaps in our understanding of how the early universe evolved.

One puzzle in question is the “Hubble tension,” which refers to a mismatch in measurements of how fast the universe is expanding. The other involves observations of numerous early, bright galaxies that existed at a time when the early universe should have been much less populated.

Now, the MIT team has found that both puzzles could be resolved if the early universe had one extra, fleeting ingredient: early dark energy. Dark energy is an unknown form of energy that physicists suspect is driving the expansion of the universe today. Early dark energy is a similar, hypothetical phenomenon that may have made only a brief appearance, influencing the expansion of the universe in its first moments before disappearing entirely.

Some physicists have suspected that early dark energy could be the key to solving the Hubble tension, as the mysterious force could accelerate the early expansion of the universe by an amount that would resolve the measurement mismatch.

The MIT researchers have now found that early dark energy could also explain the baffling number of bright galaxies that astronomers have observed in the early universe. In their new study, reported in the Monthly Notices of the Royal Astronomical Society, the team modeled the formation of galaxies in the universe’s first few hundred million years. When they incorporated a dark energy component only in that earliest sliver of time, they found the number of galaxies that arose from the primordial environment bloomed to fit astronomers’ observations.

You have these two looming open-ended puzzles,” says study co-author Rohan Naidu, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research. “We find that in fact, early dark energy is a very elegant and sparse solution to two of the most pressing problems in cosmology.”

The study’s co-authors include lead author and Kavli postdoc Xuejian (Jacob) Shen, and MIT professor of physics Mark Vogelsberger, along with Michael Boylan-Kolchin at the University of Texas at Austin, and Sandro Tacchella at the University of Cambridge.

Big city lights

Based on standard cosmological and galaxy formation models, the universe should have taken its time spinning up the first galaxies. It would have taken billions of years for primordial gas to coalesce into galaxies as large and bright as the Milky Way.

But in 2023, NASA’s James Webb Space Telescope (JWST) made a startling observation. With an ability to peer farther back in time than any observatory to date, the telescope uncovered a surprising number of bright galaxies as large as the modern Milky Way within the first 500 million years, when the universe was just 3 percent of its current age.

“The bright galaxies that JWST saw would be like seeing a clustering of lights around big cities, whereas theory predicts something like the light around more rural settings like Yellowstone National Park,” Shen says. “And we don’t expect that clustering of light so early on.”

For physicists, the observations imply that there is either something fundamentally wrong with the physics underlying the models or a missing ingredient in the early universe that scientists have not accounted for. The MIT team explored the possibility of the latter, and whether the missing ingredient might be early dark energy.

Physicists have proposed that early dark energy is a sort of antigravitational force that is turned on only at very early times. This force would counteract gravity’s inward pull and accelerate the early expansion of the universe, in a way that would resolve the mismatch in measurements. Early dark energy, therefore, is considered the most likely solution to the Hubble tension.

Galaxy skeleton

The MIT team explored whether early dark energy could also be the key to explaining the unexpected population of large, bright galaxies detected by JWST. In their new study, the physicists considered how early dark energy might affect the early structure of the universe that gave rise to the first galaxies. They focused on the formation of dark matter halos — regions of space where gravity happens to be stronger, and where matter begins to accumulate.

“We believe that dark matter halos are the invisible skeleton of the universe,” Shen explains. “Dark matter structures form first, and then galaxies form within these structures. So, we expect the number of bright galaxies should be proportional to the number of big dark matter halos.”

The team developed an empirical framework for early galaxy formation, which predicts the number, luminosity, and size of galaxies that should form in the early universe, given some measures of “cosmological parameters.” Cosmological parameters are the basic ingredients, or mathematical terms, that describe the evolution of the universe.

Physicists have determined that there are at least six main cosmological parameters, one of which is the Hubble constant — a term that describes the universe’s rate of expansion. Other parameters describe density fluctuations in the primordial soup, immediately after the Big Bang, from which dark matter halos eventually form.

The MIT team reasoned that if early dark energy affects the universe’s early expansion rate, in a way that resolves the Hubble tension, then it could affect the balance of the other cosmological parameters, in a way that might increase the number of bright galaxies that appear at early times. To test their theory, they incorporated a model of early dark energy (the same one that happens to resolve the Hubble tension) into an empirical galaxy formation framework to see how the earliest dark matter structures evolve and give rise to the first galaxies.

“What we show is, the skeletal structure of the early universe is altered in a subtle way where the amplitude of fluctuations goes up, and you get bigger halos, and brighter galaxies that are in place at earlier times, more so than in our more vanilla models,” Naidu says. “It means things were more abundant, and more clustered in the early universe.”

“A priori, I would not have expected the abundance of JWST’s early bright galaxies to have anything to do with early dark energy, but their observation that EDE pushes cosmological parameters in a direction that boosts the early-galaxy abundance is interesting,” says Marc Kamionkowski, professor of theoretical physics at Johns Hopkins University, who was not involved with the study. “I think more work will need to be done to establish a link between early galaxies and EDE, but regardless of how things turn out, it’s a clever — and hopefully ultimately fruitful — thing to try.”

We demonstrated the potential of early dark energy as a unified solution to the two major issues faced by cosmology. This might be an evidence for its existence if the observational findings of JWST get further consolidated,” Vogelsberger concludes. “In the future, we can incorporate this into large cosmological simulations to see what detailed predictions we get.”

This research was supported, in part, by NASA and the National Science Foundation.



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Plant-derived secondary organic aerosols can act as mediators of plant-plant interactions

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A new study published in Science reveals that plant-derived secondary organic aerosols (SOAs) can act as mediators of plant-plant interactions. This research was conducted through the cooperation of chemical ecologists, plant ecophysiologists and atmospheric physicists at the University of Eastern Finland.

It is well known that plants release volatile organic compounds (VOCs) into the atmosphere when damaged by herbivores. These VOCs play a crucial role in plant-plant interactions, whereby undamaged plants may detect warning signals from their damaged neighbours and prepare their defences. “Reactive plant VOCs undergo oxidative chemical reactions, resulting in the formation of secondary organic aerosols (SOAs). We wondered whether the ecological functions mediated by VOCs persist after they are oxidated to form SOAs,” said Dr. Hao Yu, formerly a PhD student at UEF, but now at the University of Bern.

The study showed that Scots pine seedlings, when damaged by large pine weevils, release VOCs that activate defences in nearby plants of the same species. Interestingly, the biological activity persisted after VOCs were oxidized to form SOAs. The results indicated that the elemental composition and quantity of SOAs likely determines their biological functions.

“A key novelty of the study is the finding that plants adopt subtly different defence strategies when receiving signals as VOCs or as SOAs, yet they exhibit similar degrees of resistance to herbivore feeding,” said Professor James Blande, head of the Environmental Ecology Research Group. This observation opens up the possibility that plants have sophisticated sensing systems that enable them to tailor their defences to information derived from different types of chemical cue.

“Considering the formation rate of SOAs from their precursor VOCs, their longer lifetime compared to VOCs, and the atmospheric air mass transport, we expect that the ecologically effective distance for interactions mediated by SOAs is longer than that for plant interactions mediated by VOCs,” said Professor Annele Virtanen, head of the Aerosol Physics Research Group. This could be interpreted as plants being able to detect cues representing close versus distant threats from herbivores.

The study is expected to open up a whole new complex research area to environmental ecologists and their collaborators, which could lead to new insights on the chemical cues structuring interactions between plants.



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Folded or cut, this lithium-sulfur battery keeps going

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What makes black holes grow and new stars form? Machine learning helps solve the mystery


Most rechargeable batteries that power portable devices, such as toys, handheld vacuums and e-bikes, use lithium-ion technology. But these batteries can have short lifetimes and may catch fire when damaged. To address stability and safety issues, researchers reporting in ACS Energy Letters have designed a lithium-sulfur (Li-S) battery that features an improved iron sulfide cathode. One prototype remains highly stable over 300 charge-discharge cycles, and another provides power even after being folded or cut.

Sulfur has been suggested as a material for lithium-ion batteries because of its low cost and potential to hold more energy than lithium-metal oxides and other materials used in traditional ion-based versions. To make Li-S batteries stable at high temperatures, researchers have previously proposed using a carbonate-based electrolyte to separate the two electrodes (an iron sulfide cathode and a lithium metal-containing anode). However, as the sulfide in the cathode dissolves into the electrolyte, it forms an impenetrable precipitate, causing the cell to quickly lose capacity. Liping Wang and colleagues wondered if they could add a layer between the cathode and electrolyte to reduce this corrosion without reducing functionality and rechargeability.

The team coated iron sulfide cathodes in different polymers and found in initial electrochemical performance tests that polyacrylic acid (PAA) performed best, retaining the electrode’s discharge capacity after 300 charge-discharge cycles. Next, the researchers incorporated a PAA-coated iron sulfide cathode into a prototype battery design, which also included a carbonate-based electrolyte, a lithium metal foil as an ion source, and a graphite-based anode. They produced and then tested both pouch cell and coin cell battery prototypes.

After more than 100 charge-discharge cycles, Wang and colleagues observed no substantial capacity decay in the pouch cell. Additional experiments showed that the pouch cell still worked after being folded and cut in half. The coin cell retained 72% of its capacity after 300 charge-discharge cycles. They next applied the polymer coating to cathodes made from other metals, creating lithium-molybdenum and lithium-vanadium batteries. These cells also had stable capacity over 300 charge-discharge cycles. Overall, the results indicate that coated cathodes could produce not only safer Li-S batteries with long lifespans, but also efficient batteries with other metal sulfides, according to Wang’s team.

The authors acknowledge funding from the National Natural Science Foundation of China; the Natural Science Foundation of Sichuan, China; and the Beijing National Laboratory for Condensed Matter Physics.



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