New insights on how galaxies are formed

Astronomers can use supercomputers to simulate the formation of galaxies from the Big Bang 13.8 billion years ago to the present day. But there are a number of sources of error. An international research team, led by researchers in Lund, has spent a hundred million computer hours over eight years trying to correct these.

The last decade has seen major advances in computer simulations that can realistically calculate how galaxies form.

These cosmological simulations are crucial to our understanding of where galaxies, stars and planets come from.

However, the predictions from such models are affected by limitations in the resolution of the simulations, as well as assumptions about a number of factors, such as how stars live and die and the evolution of the interstellar medium.

To minimise the sources of error and produce more accurate simulations, 160 researchers from 60 higher education institutions -- led by Santi Roca-Fàbrega at Lund University, Ji-hoon Kim at Seoul National University and Joel R. Primack at the University of California -- have collaborated and now present the results of the largest comparison of simulations done ever.

"To make progress towards a theory of galaxy formation, it is crucial to compare results and codes from different simulations. We have now done this by bringing together competing code groups behind the world's best galaxy simulators in a kind of supercomparison," says Santi Roca-Fàbrega, a researcher in astrophysics.

Three papers from this collaboration, known as the CosmoRun simulations, have now been published in The Astrophysical Journal. In these, the researchers have analysed the formation of a galaxy with the same mass as the Milky Way.

The simulation is based on the same astrophysical assumptions about the ultraviolet background radiation produced by the first stars in the Universe, the gas cooling and heating, and the process of star formation.

The new results allow the researchers to conclude that disc galaxies like the Milky Way formed very early in the history of the Universe, in line with observations from the James Webb Telescope.

They have also found a way to make the number of satellite galaxies -- galaxies orbiting larger galaxies -- consistent with observations finally solving a problem well known in the community and known as "the missing satellites problem."

In addition, the team has revealed how the gas surrounding galaxies is the key to realistic simulations, rather than the number and distribution of stars, which had previously been the standard.

"The work has been going on for the past eight years and has entailed running hundreds of simulations and using a hundred million hours of supercomputing facilities," says Santi Roca-Fàbrega.

Now the journey continues to further refine the simulations of galaxy formation.

With each technological achievement, Santi Roca-Fàbrega and his colleagues hope to add new pieces to the dizzying puzzle of the birth and evolution of the universe and galaxies.

Read more at Science Daily

Machine learning reveals how black holes grow

As different as they may seem, black holes and Las Vegas have one thing in common: What happens there stays there -- much to the frustration of astrophysicists trying to understand how, when and why black holes form and grow. Black holes are surrounded by a mysterious, invisible layer -- the event horizon -- from which nothing can escape, be it matter, light or information. The event horizon swallows every bit of evidence about the black hole's past.

"Because of these physical facts, it had been thought impossible to measure how black holes formed," said Peter Behroozi, an associate professor at the University of Arizona Steward Observatory and a project researcher at the National Astronomical Observatory of Japan.

Together with Haowen Zhang, a doctoral student at Steward, Behroozi led an international team to use machine learning and supercomputers to reconstruct the growth histories of black holes, effectively peeling back their event horizons to reveal what lies beyond.

Simulations of millions of computer-generated "universes" revealed that supermassive black holes grow in lockstep with their host galaxies. This had been suspected for 20 years, but scientists had not been able to confirm this relationship until now. A paper with the team's findings has been published in Monthly Notices of the Royal Astronomical Society.

"If you go back to earlier and earlier times in the universe, you find that exactly the same relationship was present," said Behroozi, a co-author on the paper. "So, as the galaxy grows from small to large, its black hole, too, is growing from small to large, in exactly the same way as we see in galaxies today all across the universe."

Most, if not all, galaxies scattered throughout the cosmos are thought to harbor a supermassive black hole at their center. These black holes pack masses greater than 100,000 times that of the sun, with some boasting millions, even billions of solar masses. One of astrophysics' most vexing questions has been how these behemoths grow as fast they do, and how they form in the first place.

To find answers, Zhang, Behroozi and their colleagues created Trinity, a platform that uses a novel form of machine learning capable of generating millions of different universes on a supercomputer, each of which obeys different physical theories for how galaxies should form. The researchers built a framework in which computers propose new rules for how supermassive black holes grow over time. They then used those rules to simulate the growth of billions of black holes in a virtual universe and "observed" the virtual universe to test whether it agreed with decades of actual observations of black holes across the real universe. After millions of proposed and rejected rule sets, the computers settled on rules that best described existing observations.

"We're trying to understand the rules of how galaxies form," Behroozi said. "In a nutshell, we make Trinity guess what the physical laws may be and let them go in a simulated universe and see how that universe turns out. Does it look anything like the real one or not?"

According to the researchers, this approach works equally well for anything else inside of the universe, not just galaxies.

The project's name, Trinity, is in reference to its three main areas of study: galaxies, their supermassive black holes and their dark matter halos -- vast cocoons of dark matter that are invisible to direct measurements but whose existence is necessary to explain the physical characteristics of galaxies everywhere. In previous studies, the researchers used an earlier version of their framework, called the UniverseMachine, to simulate millions of galaxies and their dark matter halos. The team discovered that galaxies growing in their dark matter halos follow a very specific relationship between the mass of the halo and the mass of the galaxy.

"In our new work, we added black holes to this relationship," Behroozi said, "and then asked how black holes could grow in those galaxies to reproduce all the observations people have made about them."

"We have very good observations of black hole masses," said Zhang, the paper's lead author. "However, those are largely restricted to the local universe. As you look farther away, it becomes increasingly difficult, and eventually impossible, to accurately measure the relationships between the masses of black holes and their host galaxies. Because of that uncertainty, observations can't directly tell us whether that relationship holds up throughout the universe."

Trinity allows astrophysicists to sidestep not only that limitation, but also the event horizon information barrier for individual black holes by stitching together information from millions of observed black holes at different stages of their growth. Even though no individual black hole's history could be reconstructed, the researchers could measure the average growth history of all black holes taken together.

"If you put black holes into the simulated galaxies and enter rules about how they grow, you can compare the resulting universe to all the observations of actual black holes that we have," Zhang said. "We can then reconstruct how any black hole and galaxy in the universe looked from today back to the very beginning of the cosmos."

The simulations shed light on another puzzling phenomenon: Supermassive black holes -- like the one found in the center of the Milky Way -- grew most vigorously during their infancy, when the universe was only a few billion years old, only to slow down dramatically during the ensuing time, over the last 10 billion years or so.

"We've known for a while that galaxies have this strange behavior, where they reach a peak in their rate of forming new stars, then it dwindles over time, and then, later on, they stop forming stars altogether," Behroozi said. "Now, we've been able to show that black holes do the same: growing and shutting off at the same times as their host galaxies. This confirms a decades-old hypothesis about black hole growth in galaxies."

However, the result poses more questions, he added. Black holes are much smaller than the galaxies in which they live. If the Milky Way were scaled down to the size of Earth, its supermassive black hole would be the size of the period at the end of this sentence.

For the black hole to double in mass within the same timeframe as the larger galaxy requires synchronization between gas flows at vastly different scales. How black holes conspire with galaxies to achieve this balance is yet to be understood.

Read more at Science Daily

Researchers reveal the origin story for carbon-12, a building block for life

With the help of the world's most powerful supercomputer and new artificial intelligence techniques, an international team of researchers has theorized how the extreme conditions in stars produce carbon-12, which they describe as "a critical gateway to the birth of life."

The researchers' fundamental question: "How does the cosmos produce carbon-12?" said James Vary, a professor of physics and astronomy at Iowa State University and a longtime member of the research collaboration.

"It turns out it's not easy to produce carbon-12," Vary said.

It takes the extreme heat and pressures inside stars or in stellar collisions and explosions to create emergent, unstable, excited-state carbon nuclei with three loosely linked clumps, each with two protons and two neutrons. A fraction of those unstable carbon nuclei can shoot off a little extra energy in the form of gamma rays and become stable carbon-12, the stuff of life.

A paper recently published by the online journal Nature Communications describes the researchers' supercomputer simulations and resulting theory for the nuclear structure of carbon that favors its formation in the cosmos. The corresponding author is Takaharu Otsuka of the University of Tokyo, the RIKEN Nishina Center for Accelerator-Based Science and the Advanced Science Research Center of the Japan Atomic Energy Agency.

The paper describes how alpha particles -- helium-4 atoms, with two protons and two neutrons -- can cluster to form much heavier atoms, including an unstable, excited carbon-12 state known as the Hoyle state (predicted by theoretical astrophysicist Fred Hoyle in 1953 as a precursor to life as we know it).

The researchers write that this alpha-particle clustering "is a very beautiful and fascinating idea and is indeed plausible because the (alpha) particle is particularly stable with a large binding energy."

To test the theory, the researchers ran supercomputer simulations, including calculations on the Fugaku supercomputer at the RIKEN Center for Computational Science in Kobe, Japan. Fugaku is listed as the most powerful supercomputer in the world and is three times more powerful than No. 2, according to the latest TOP500 supercomputer rankings.

Vary said the researchers also did their work ab initio, or from first principles, meaning their calculations were based on known science and didn't include additional assumptions or parameters.

They also developed techniques in statistical learning, a branch of computational artificial intelligence, to reveal alpha clustering the Hoyle state and the eventual production of stable carbon-12.

Vary said the team has worked for more than a decade to develop its software, refine its supercomputer codes, run its calculations and work out smaller problems while building up to the current work.

"There's a lot of subtlety -- a lot of beautiful interactions going on in there," Vary said.

All the calculations, physical quantities and theoretical subtlety match what experimental data there is in this corner of nuclear physics, the researchers wrote.

So they think they have some basic answers about the origins of carbon-12. Vary said that should lead to more studies looking for "fine-grain detail" about the process and how it works.

Was carbon production, for example, mostly the result of internal processes in stars? Vary asked. Or was it supernova star explosions? Or collisions of super-dense neutron stars?

Read more at Science Daily

Bay Area storms get wetter in a warming world

The December 2014 North American Storm Complex was a powerful winter storm, referred to by some as California's "Storm of the Decade." Fueled by an atmospheric river originating over the tropical waters of the Pacific Ocean, the storm dropped 8 inches of rainfall in 24 hours, sported wind gusts of 139 miles per hour, and left 150,000 households without power across the San Francisco Bay Area.

Writing in Weather and Climate Extremes this week, researchers described the potential impacts of climate change on extreme storms in the San Francisco Bay area, among them the December 2014 North American Storm Complex.

Re-simulating five of the most powerful storms that have hit the area, they determined that under future conditions some of these extreme events would deliver 26-37% more rain, even more than is predicted simply by accounting for air's ability to carry more water in warmer conditions.

However, they found these increases would not occur with every storm, only those that include an atmospheric river accompanied by an extratropical cyclone.

The research -- funded by the City and County of San Francisco and in partnership with agencies including the San Francisco Public Utilities Commission, Port of San Francisco, and San Francisco International Airport -- will help the region plan its future infrastructure with mitigation and sustainability in mind.

"Having this level of detail is a game changer," said Dennis Herrera, General Manager of the San Francisco Public Utilities Commission, which was the lead City agency on the study. "This groundbreaking data will help us develop tools to allow our port, airport, utilities, and the City as a whole to adapt to our changing climate and increasingly extreme storms."

These first-of-their-kind forecasts for the city were made possible by the Stampede2 supercomputer at the Texas Advanced Computing Center (TACC) and the Cori system at the National Energy Research Scientific Computing Center (NERSC) -- two of the most powerful supercomputers in the world, supported by the National Science Foundation and Department of Energy respectively.

Hindcasting With the Future in Mind

Certain facets of our future climate are well established -- higher temperatures, rising seas, species loss. But how will greater greenhouse gas concentrations and warmer air and oceans effect extreme weather, like hurricanes, tornadoes, and heavy rainfall? And where precisely will these changes be the greatest and under what conditions?

Forecasting the natural hazards of the future is the mission of Christina Patricola, Assistant Professor of Geological and Atmospheric Sciences at Iowa State University and lead author on the Weather and Climate Extremes paper. Her research helps quantify and understand the risks we face from natural hazards in the future.

Using supercomputers allowed Patricola to model the region with 3 kilometer resolution. Scientists believe this level of detail is needed to capture the dynamics of storm systems like hurricanes and atmospheric rivers, and to predict their impact on an urban area.

For each of the historical storms, Patricola and her collaborators ran 10-member ensembles -- independent, slightly different simulations -- with 3 kilometer resolution, a process called 'hindcasting' (as opposed to forecasting). They then adjusted the greenhouse gas concentrations and sea-surface temperatures to predict how these historical storms would look in the projected future climates of 2050 and 2100.

Patricola calls these "storyline" experiments: computer models that are meant to be instructive for thinking about how historically-impactful storm events could look in a warmer world. Focusing on events that were known to be impactful to city operations provides a useful context for understanding the potential impacts of events if they occurred under future climate conditions.

The study doesn't address changes in the frequency of extreme storms in the future and therefore can't address how precipitation will change overall, she said. (Another pressing question for California planners.) But they can help decision-makers understand trends in the intensity of the worst-case-scenario storms and make informed choices.

On the West Coast, much of the precipitation that falls is associated with atmospheric rivers (ARs), which transport a substantial amount of moisture in a narrow band, Patricola explained. Some of the storms they looked at featured ARs alone. Others had ARs at the same time as low-pressure systems known as extratropical cyclones (ETCs).

"We found something very interesting," she said. "Precipitation increased substantially for events with an atmospheric river and a cyclone together, whereas precipitation changes were weak or negative when there was only an atmospheric river."

The difference, she believes, lies in the lifting mechanism. In general, heavy precipitation requires moist air to ascend. While the AR-only storms showed a future increase in atmospheric moisture, the storms with an AR and ETC showed a future increase in atmospheric moisture and rising air. Additional investigations will explore this relationship.

High Performance Climate Science

Patricola has used TACC supercomputers for climate and weather modeling since 2010, when she was a graduate student at Cornell University working with leading climate scientist, Kerry Cook (now at The University of Texas at Austin). She recalls that her first models had a horizontal resolution of 90 km -- 30 times less resolved than today -- and were considered state-of-the-art at the time.

"It was a very big help to have the resource from TACC and NERSC for these simulations," she said. "We're interested in extreme precipitation totals and hourly rainfall rates. We had to go to a high resolution of 3 km to make these predictions. And as we increase resolution, the computational expense goes up."

Patricola has used the methodology she developed to understand other phenomena, like how tropical cyclones may change in the future. She and collaborator Michael Wehner reported on these changes in a 2018 Nature paper. "If a hurricane like Katrina happened at the end of the 21st century, what could it be like? More rainfall, higher winds? Our method can be used for any type of weather system that can be hindcasted."

In the next phase of the San Francisco project, Patricola will work with city staff and their collaborators to understand what the weather changes mean in terms of city operations.

Read more at Science Daily

How disorderly young galaxies grow up and mature

Using a supercomputer simulation, a research team at Lund University in Sweden has succeeded in following the development of a galaxy over a span of 13.8 billion years. The study shows how, due to interstellar frontal collisions, young and chaotic galaxies over time mature into spiral galaxies such as the Milky Way.

Soon after the Big Bang 13.8 billion years ago, the Universe was an unruly place. Galaxies constantly collided. Stars formed at an enormous rate inside gigantic gas clouds. However, after a few billion years of intergalactic chaos, the unruly, embryonic galaxies became more stable and over time matured into well-ordered spiral galaxies. The exact course of these developments has long been a mystery to the world's astronomers. However, in a new study published in Monthly Notices of the Royal Astronomical Society, researchers have been able to provide some clarity on the matter.

"Using a supercomputer, we have created a high-resolution simulation that provides a detailed picture of a galaxy's development since the Big Bang, and how young chaotic galaxies transition into well-ordered spirals" says Oscar Agertz, astronomy researcher at Lund University.

In the study, the astronomers, led by Oscar Agertz and Florent Renaud, use the Milky Way's stars as a starting point. The stars act as time capsules that divulge secrets about distant epochs and the environment in which they were formed. Their positions, speeds and amounts of various chemical elements can therefore, with the assistance of computer simulations, help us understand how our own galaxy was formed.

"We have discovered that when two large galaxies collide, a new disc can be created around the old one due to the enormous inflows of star-forming gas. Our simulation shows that the old and new discs slowly merged over a period of several billion years. This is something that not only resulted in a stable spiral galaxy, but also in populations of stars that are similar to those in the Milky Way," says Florent Renaud, astronomy researcher at Lund University.

The new findings will help astronomers to interpret current and future mappings of the Milky Way. The study points to a new direction for research in which the main focus will be on the interaction between large galaxy collisions and how spiral galaxies' discs are formed. The research team in Lund has already started new super computer simulations in cooperation with the research infrastructure PRACE (Partnership for Advanced Computing in Europe).

Read more at Science Daily

Cracking a mystery of massive black holes and quasars with supercomputer simulations

At the center of galaxies, like our own Milky Way, lie massive black holes surrounded by spinning gas. Some shine brightly, with a continuous supply of fuel, while others go dormant for millions of years, only to reawaken with a serendipitous influx of gas. It remains largely a mystery how gas flows across the universe to feed these massive black holes.

UConn Assistant Professor of Physics Daniel Anglés-Alcázar, lead author on a paper published today in The Astrophysical Journal, addresses some of the questions surrounding these massive and enigmatic features of the universe by using new, high-powered simulations.

"Supermassive black holes play a key role in galaxy evolution and we are trying to understand how they grow at the centers of galaxies," says Anglés-Alcázar. "This is very important not just because black holes are very interesting objects on their own, as sources of gravitational waves and all sorts of interesting stuff, but also because we need to understand what the central black holes are doing if we want to understand how galaxies evolve."

Anglés-Alcázar, who is also an Associate Research Scientist at the Flatiron Institute Center for Computational Astrophysics, says a challenge in answering these questions has been creating models powerful enough to account for the numerous forces and factors that play into the process. Previous works have looked either at very large scales or the very smallest of scales, "but it has been a challenge to study the full range of scales connected simultaneously."

Galaxy formation, Anglés-Alcázar says, starts with a halo of dark matter that dominates the mass and gravitational potential in the area and begins pulling in gas from its surroundings. Stars form from the dense gas, but some of it must reach the center of the galaxy to feed the black hole. How does all that gas get there? For some black holes, this involves huge quantities of gas, the equivalent of ten times the mass of the sun or more swallowed in just one year, says Anglés-Alcázar.

"When supermassive black holes are growing very fast, we refer to them as quasars," he says. "They can have a mass well into one billion times the mass of the sun and can outshine everything else in the galaxy. How quasars look depends on how much gas they add per unit of time. How do we manage to get so much gas down to the center of the galaxy and close enough that the black hole can grab it and grow from there?"

The new simulations provide key insights into the nature of quasars, showing that strong gravitational forces from stars can twist and destabilize the gas across scales, and drive sufficient gas influx to power a luminous quasar at the epoch of peak galaxy activity.

In visualizing this series of events, it is easy to see the complexities of modeling them, and Anglés-Alcázar says it is necessary to account for the myriad components influencing black hole evolution.

"Our simulations incorporate many of the key physical processes, for example, the hydrodynamics of gas and how it evolves under the influence of pressure forces, gravity, and feedback from massive stars. Powerful events such as supernovae inject a lot of energy into the surrounding medium and this influences how the galaxy evolves, so we need to incorporate all of these details and physical processes to capture an accurate picture."

Building on previous work from the FIRE ("Feedback In Realistic Environments") project, Anglés-Alcázar explains the new technique outlined in the paper that greatly increases model resolution and allows for following the gas as it flows across the galaxy with more than a thousand times better resolution than previously possible,

"Other models can tell you a lot of details about what's happening very close to the black hole, but they don't contain information about what the rest of the galaxy is doing, or even less, what the environment around the galaxy is doing. It turns out, it is very important to connect all of these processes at the same time, this is where this new study comes in."

The computing power is similarly massive, Anglés-Alcázar says, with hundreds of central processing units (CPUs) running in parallel that could have easily taken the length of millions of CPU hours.

"This is the first time that we have been able to create a simulation that can capture the full range of scales in a single model and where we can watch how gas is flowing from very large scales all the way down to the very center of the massive galaxy that we are focusing on."

For future studies of large statistical populations of galaxies and massive black holes, we need to understand the full picture and the dominant physical mechanisms for as many different conditions as possible, says Anglés-Alcázar.

Read more at Science Daily

Desktop PCs run simulations of mammals' brains

 University of Sussex academics have established a method of turbocharging desktop PCs to give them the same capability as supercomputers worth tens of millions of pounds.

Dr James Knight and Prof Thomas Nowotny from the University of Sussex's School of Engineering and Informatics used the latest Graphical Processing Units (GPUs) to give a single desktop PC the capacity to simulate brain models of almost unlimited size.

The researchers believe the innovation, detailed in Nature Computational Science, will make it possible for many more researchers around the world to carry out research on large-scale brain simulation, including the investigation of neurological disorders.

Currently, the cost of supercomputers is so prohibitive they are only affordable to very large institutions and government agencies and so are not accessible for large numbers of researchers.

As well as shaving tens of millions of pounds off the costs of a supercomputer, the simulations run on the desktop PC require approximately 10 times less energy bringing a significant sustainability benefit too.

Dr Knight, Research Fellow in Computer Science at the University of Sussex, said: "I think the main benefit of our research is one of accessibility. Outside of these very large organisations, academics typically have to apply to get even limited time on a supercomputer for a particular scientific purpose. This is quite a high barrier for entry which is potentially holding back a lot of significant research.

"Our hope for our own research now is to apply these techniques to brain-inspired machine learning so that we can help solve problems that biological brains excel at but which are currently beyond simulations.

"As well as the advances we have demonstrated in procedural connectivity in the context of GPU hardware, we also believe that there is also potential for developing new types of neuromorphic hardware built from the ground up for procedural connectivity. Key components could be implemented directly in hardware which could lead to even more truly significant compute time improvements."

The research builds on the pioneering work of US researcher Eugene Izhikevich who pioneered a similar method for large-scale brain simulation in 2006.

At the time, computers were too slow for the method to be widely applicable meaning simulating large-scale brain models has until now only been possible for a minority of researchers privileged to have access to supercomputer systems.

The researchers applied Izhikevich's technique to a modern GPU, with approximately 2,000 times the computing power available 15 years ago, to create a cutting-edge model of a Macaque's visual cortex (with 4.13 × 106 neurons and 24.2 × 109 synapse) which previously could only be simulated on a supercomputer.

The researchers' GPU accelerated spiking neural network simulator uses the large amount of computational power available on a GPU to 'procedurally' generate connectivity and synaptic weights 'on the go' as spikes are triggered -- removing the need to store connectivity data in memory.

Initialization of the researchers' model took six minutes and simulation of each biological second took 7.7 min in the ground state and 8.4 min in the resting state- up to 35 % less time than a previous supercomputer simulation. In 2018, one rack of an IBM Blue Gene/Q supercomputer initialization of the model took around five minutes and simulating one second of biological time took approximately 12 minutes.

Prof Nowotny, Professor of Informatics at the University of Sussex, said: "Large-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 × 1012 synaptic connections meaning that simulations require several terabytes of data -- an unrealistic memory requirement for a single desktop machine.

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Supercomputer simulations could unlock mystery of Moon's formation

 Astronomers have taken a step towards understanding how the Moon might have formed out of a giant collision between the early Earth and another massive object 4.5 billion years ago.

Scientists led by Durham University, UK, ran supercomputer simulations on the DiRAC High-Performance Computing facility to send a Mars-sized planet -- called Theia -- crashing into the early Earth.

Their simulations produced an orbiting body that could potentially evolve into a Moon-like object.

While the researchers are careful to say that this is not definitive proof of the Moon's origin, they add that it could be a promising stage in understanding how our nearest neighbour might have formed.

The findings are published in the journal Monthly Notices of the Royal Astronomical Society.

The Moon is thought to have formed in a collision between the early Earth and Theia, which scientists believe might have been an ancient planet in our solar system, about the size of Mars.

Researchers ran simulations to track material from the early Earth and Theia for four days after their collision, then ran other simulations after spinning Theia like a pool ball.

The simulated collision with the early Earth produced different results depending upon the size and direction of Theia's initial spin.

At one extreme the collision merged the two objects together while at the other there was a grazing hit-and-run impact.

Importantly, the simulation where no spin was added to Theia produced a self-gravitating clump of material with a mass of about 80 per cent of the Moon, while another Moon-like object was created when a small amount of spin was added.

The resulting clump, which settles into an orbit around the post-impact Earth, would grow by sweeping up the disc of debris surrounding our planet.

The simulated clump also has a small iron core, similar to that of the Moon, with an outer layer of materials made up from the early Earth and Theia.

Recent analysis of oxygen isotope ratios in the lunar samples collected by the Apollo space missions suggests that a mixture of early Earth and impactor material might have formed the Moon.

Lead author Sergio Ruiz-Bonilla, a PhD researcher in Durham University's Institute for Computational Cosmology, said: "By adding different amounts of spin to Theia in simulations, or by having no spin at all, it gives you a whole range of different outcomes for what might have happened when the early Earth was hit by a massive object all those billions of years ago.

"It's exciting that some of our simulations produced this orbiting clump of material that is relatively not much smaller than the Moon, with a disc of additional material around the post-impact Earth that would help the clump grow in mass over time.

"I wouldn't say that this is the Moon, but it's certainly a very interesting place to continue looking."

The Durham-led research team now plan to run further simulations altering the mass, speed and spinning rate of both the target and impactor to see what effect this has on the formation of a potential Moon.

Co-author Dr Vincent Eke, of Durham University's Institute for Computational Cosmology, said: "We get a number of different outcomes depending upon whether or not we introduce spin to Theia before it crashes into the early Earth.

"It's particularly fascinating that when no spin or very little spin is added to Theia that the impact with the early Earth leaves a trail of debris behind, which in some cases includes a body large enough to deserve being called a proto-Moon.

Read more at Science Daily

Targeting the shell of the Ebola virus

 As the world grapples with the coronavirus (COVID-19) pandemic, another virus has been raging again in the Democratic Republic of the Congo in recent months: Ebola. Since the first terrifying outbreak in 2013, the Ebola virus has periodically emerged in Africa, causing horrific bleeding in its victims and, in many cases, death.

How can we battle these infectious agents that reproduce by hijacking cells and reprogramming them into virus-replicating machines? Science at the molecular level is critical to gaining the upper hand -- research you'll find underway in the laboratory of Professor Juan Perilla at the University of Delaware.

Perilla and his team of graduate and undergraduate students in UD's Department of Chemistry and Biochemistry are using supercomputers to simulate the inner workings of Ebola, observing the way molecules move, atom by atom, to carry out their functions. In the team's latest work, they reveal structural features of the virus's coiled protein shell, or nucleocapsid, that may be promising therapeutic targets, more easily destabilized and knocked out by an antiviral treatment.

The research is highlighted in the Tuesday, Oct. 20 issue of the Journal of Chemical Physics, which is published by the American Institute of Physics, a federation of societies in the physical sciences representing more than 120,000 members.

"The Ebola nucleocapsid looks like a Slinky walking spring, whose neighboring rings are connected," Perilla said. "We tried to find what factors control the stability of this spring in our computer simulations."

The life cycle of Ebola is highly dependent on this coiled nucleocapsid, which surrounds the virus's genetic material consisting of a single strand of ribonucleic acid (ssRNA). Nucleoproteins protect this RNA from being recognized by cellular defense mechanisms. Through interactions with different viral proteins, such as VP24 and VP30, these nucleoproteins form a minimal functional unit -- a copy machine -- for viral transcription and replication.

While nucleoproteins are important to the nucleocapsid's stability, the team's most surprising finding, Perilla said, is that in the absence of single-stranded RNA, the nucleocapsid quickly becomes disordered. But RNA alone is not sufficient to stabilize it. The team also observed charged ions binding to the nucleocapsid, which may reveal where other important cellular factors bind and stabilize the structure during the virus's life cycle.

Perilla compared the team's work to a search for molecular "knobs" that control the nucleocapsid's stability like volume control knobs that can be turned up to hinder virus replication.

The UD team built two molecular dynamics systems of the Ebola nucleocapsid for their study. One included single-stranded RNA; the other contained only the nucleoprotein. The systems were then simulated using the Texas Advanced Computing Center's Frontera supercomputer -- the largest academic supercomputer in the world. The simulations took about two months to complete.

Graduate research assistant Chaoyi Xu ran the molecular simulations, while the entire team was involved in developing the analytical framework and conducting the analysis. Writing the manuscript was a learning experience for Xu and undergraduate research assistant Tanya Nesterova, who had not been directly involved in this work before. She also received training as a next-generation computational scientist with support from UD's Undergraduate Research Scholars program and NSF's XSEDE-EMPOWER program. The latter has allowed her to perform the highest-level research using the nation's top supercomputers. Postdoctoral researcher Nidhi Katyal's expertise also was essential to bringing the project to completion, Perilla said.

While a vaccine exists for Ebola, it must be kept extremely cold, which is difficult in remote African regions where outbreaks have occurred. Will the team's work help advance new treatments?

"As basic scientists we are excited to understand the fundamental principles of Ebola," Perilla said. "The nucleocapsid is the most abundant protein in the virus and it's highly immunogenic -- able to produce an immune response. Thus, our new findings may facilitate the development of new antiviral treatments."

Read more at Science Daily

Conditions that trigger supernovae explosions

Understanding the thermonuclear explosion of Type Ia supernovae -- powerful and luminous stellar explosions -- is only possible through theoretical models, which previously were not able to account for the mechanism that detonated the explosion.

One of the key pieces of this explosion, present virtually in all models, is the formation of a supersonic reaction wave called detonation, which can travel faster than the speed of sound and is capable of burning up all of the material of a star before it gets dispersed into the vacuum of space.

But, the physics of the mechanisms that create a detonation in a star has been elusive.

Now, a team of researchers from the University of Connecticut, Texas A&M University, University of Central Florida, Naval Research Laboratory, and Air Force Research Laboratory has developed a theory that sheds light on the enigmatic process of detonation formation at the heart of these remarkable astronomical events.

The research, published Nov. 1 in Science, offers a critical understanding of this physical process both in stars and also in chemical systems on Earth. It was led by Alexei Poludnenko, UConn School of Engineering and Texas A&M University; in collaboration with Jessica Chambers and Kareem Ahmed, the University of Central Florida; Vadim Gamezo, the Naval Research Laboratory; and Brian Taylor, the Air Force Research Laboratory.

For the first time, researchers were able to demonstrate the process of detonation formation from a slow subsonic flame using both experiments and numerical simulations carried out on some of the largest supercomputers in the nation. They also successfully applied the results to predict the conditions of detonation formation in one of the classical theoretical scenarios of Type Ia supernova explosion.

Type Ia supernovae explosions happen when carbon and oxygen packed to a density of around 1,000 tons per cubic centimeter in the stellar core burn in quick, thermonuclear reactions. The resulting explosion disrupts a star in a matter of seconds and ejects most of its mass while emitting an amount of energy equal to the energy emitted by the star over its entire lifetime.

Typically, in order to form a detonation, burning must occur in a confined setting with walls, obstacles, or boundaries, which can confine pressure waves being released by burning.

As pressure rises, shock waves form, which can grow in strength to the point when they can compress the reacting mixture igniting it and producing a self-sustaining supersonic front. Stars do not have walls or obstacles, which makes the formation of a detonation enigmatic.

In this study, the team developed a unified theory of turbulence-induced deflagration-to-detonation that describes the mechanism and conditions for initiating detonation both in unconfined chemical and thermonuclear explosions.

According to the theory, if one takes reactive mixture, which burns and releases energy, and stirs it up to create intense turbulence, a catastrophic instability can result and would rapidly increase pressure in the system producing strong shocks and igniting a detonation. Remarkably this theory predicts the conditions for detonation formation in Type Ia supernovae.

Researchers were able to gain insight into the fundamental aspects of the physical processes that control supernovae explosions because thermonuclear combustion waves are similar to chemical combustion waves on Earth in that they are controlled by the same physical mechanisms.

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Supercomputers use graphics processors to solve longstanding turbulence question

Advanced simulations have solved a problem in turbulent fluid flow that could lead to more efficient turbines and engines.

When a fluid, such as water or air, flows fast enough, it will experience turbulence -- seemingly random changes in velocity and pressure within the fluid.

Turbulence is extremely difficult to study but is important for many fields of engineering, such as air flow past wind turbines or jet engines. Understanding turbulence better would allow engineers to design more efficient turbine blades, for example, or make more aerodynamic shapes for Formula 1 cars.

However, current engineering models of turbulence often rely upon 'empirical' relationships based on previous observations of turbulence to predict what will happen, rather than a full understanding of the underlying physics.

This is because the underlying physics is immensely complicated, leaving many questions that seem simple unsolved.

Now, researchers at Imperial College London have used supercomputers, running simulations on graphics processors originally developed for gaming, to solve a longstanding question in turbulence.

Their result, published today in the Journal of Fluid Mechanics, means empirical models can be tested and new models can be created, leading to more optimal designs in engineering.

Co-author Dr Peter Vincent, from the Department of Aeronautics at Imperial, said: "We now have a solution for an important fundamental flow problem. This means we can check empirical models of turbulence against the 'correct' answer, to see how well they are describing what actually happens, or if they need adjusting."

The question is quite simple: if a turbulent fluid is flowing in a channel and it is disturbed, how does that disturbance dissipate in the fluid? For example, if water was suddenly released from a dam into a river and then shut off, what affect would that pulse of dam water have on the flow of the river?

To determine the overall 'average' behaviour of the fluid response, the team needed to simulate the myriad smaller responses within the fluid. They used supercomputers to run thousands of turbulent flow simulations, each requiring billions of calculations to complete.

Using these simulations, they were able to determine the exact parameters that describe how the disturbance dissipates in the flow and determined various requirements that empirical turbulence models must satisfy.

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Supercomputer shows 'Chameleon Theory' could change how we think about gravity

Andromeda Galaxy

Supercomputer simulations of galaxies have shown that Einstein's theory of General Relativity might not be the only way to explain how gravity works or how galaxies form.

Physicists at Durham University, UK, simulated the cosmos using an alternative model for gravity -- f(R)-gravity, a so called Chameleon Theory.

The resulting images produced by the simulation show that galaxies like our Milky Way could still form in the universe even with different laws of gravity.

The findings show the viability of Chameleon Theory -- so called because it changes behaviour according to the environment -- as an alternative to General Relativity in explaining the formation of structures in the universe.

The research could also help further understanding of dark energy -- the mysterious substance that is accelerating the expansion rate of the universe.

The findings are published in Nature Astronomy.

General Relativity was developed by Albert Einstein in the early 1900s to explain the gravitational effect of large objects in space, for example to explain the orbit of Mercury in the solar system.

It is the foundation of modern cosmology but also plays a role in everyday life, for example in calculating GPS positions in smartphones.

Scientists already know from theoretical calculations that Chameleon Theory can reproduce the success of General Relativity in the solar system.

The Durham team has now shown that this theory allows realistic galaxies like our Milky Way to form and can be distinguished from General Relativity on very large cosmological scales.

Research co-lead author Dr Christian Arnold, in Durham University's Institute for Computational Cosmology, said: "Chameleon Theory allows for the laws of gravity to be modified so we can test the effect of changes in gravity on galaxy formation.

"Through our simulations we have shown for the first time that even if you change gravity, it would not prevent disc galaxies with spiral arms from forming.

"Our research definitely does not mean that General Relativity is wrong, but it does show that it does not have to be the only way to explain gravity's role in the evolution of the universe."

The researchers looked at the interaction between gravity in Chameleon Theory and supermassive black holes that sit at the centre of galaxies.

Black holes play a key role in galaxy formation because the heat and material they eject when swallowing surrounding matter can burn away the gas needed to form stars, effectively stopping star formation.

The amount of heat spewed out by black holes is altered by changing gravity, affecting how galaxies form.

However, the new simulations showed that even accounting for the change in gravity caused by applying Chameleon Theory, galaxies were still be able to form.

General Relativity also has consequences for understanding the accelerating expansion of the universe.

Scientists believe this expansion is being driven by dark energy and the Durham researchers say their findings could be a small step towards explaining the properties of this substance.

Research co-lead author Professor Baojiu Li, of Durham University's Institute for Computational Cosmology, said: "In General Relativity, scientists account for the accelerated expansion of the universe by introducing a mysterious form of matter called dark energy -- the simplest form of which may be a cosmological constant, whose density is a constant in space and time.

"However, alternatives to a cosmological constant which explain the accelerated expansion by modifying the law of gravity, like f(R) gravity, are also widely considered given how little is known about dark energy."

The Durham researchers expect their findings can be tested through observations using the Square Kilometre Array (SKA) telescope, based in Australia and South Africa, which is due to begin observations in 2020.

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