Moon's magnetic fields are the remnant of an ancient core dynamo

Supercomputer simulations show that alternative explanatory models such as asteroid impacts do not generate sufficiently large magnetic fields

Presently, the moon does not have an internal magnetic field as it can be observed on Earth. However, there are localized regions on its surface up to several hundred kilometers in size where a very strong magnetic field prevails. This has been shown by measurements on rocks from the Apollo missions. Since then, research has puzzled about the origin of these magnetic spots. One theory is that they are in some way remnants of an ancient core magnetic field. Possibly similar to what can still be observed on Earth today. Here, the core consists of molten and solid iron and its rotation generates the earth's magnetic field. Why the inner field of the Moon has extinguished at some point remains a subject of research.

Another long-discussed theory about the local magnetic spots of the moon suggests that they are the result of magnetization processes caused by the impacts of massive bodies on the moon surface. A study recently published in the journal Science Advances now shows, that the Moon must have had an internal core dynamo in the past. The researchers came to their conclusion by disproving this second theory with the help of complex supercomputer simulations. It is the result of large international cooperation between MIT, GFZ-Potsdam, UCLA, the University of Potsdam, the University of Michigan, and the Australian Curtin University. {module INSIDE STORY}

The second thesis was supported among other things by the fact that large and strong magnetic spots were found on the other side of the moon, exactly opposite large lunar craters. Their origin was assumed to be as follows: Because the Moon - unlike the Earth - has no atmosphere to protect it from meteorites and asteroids, such massive bodies can hit it with full force and pulverize and ionize material on its surface. A cloud of charged particles, also called plasma, created in this way flows around the Moon, compresses the magnetic solar wind present in space, and thus strengthens its magnetic field. At the same time, the solar wind induces a magnetic field in the moon itself. At the surface opposite the impact, all these fields are amplified and create the observed magnetism in the crustal rock.

Using the examples of some well-known Moon craters like the one we regard as its "right eye", the researchers have now simulated the impact including the plasma formation, the propagation of the plasma around the moon, and the course of the field induced in the moon's interior. Using software that was originally developed for space physics and space weather applications, they simulated very different impact scenarios. In this way, the scientists were able to show that the amplification of the magnetic fields due to collisions and ejected material alone was not sufficient to generate the large field strengths as originally estimated and measured on the moon: The resulting magnetic field is a thousand times weaker than necessary to explain the observations. This does not mean, however, that these effects do not exist; they are only comparatively weak. In particular, the simulations showed that the field amplification by the plasma cloud on the rear side of the impact is more likely to occur above the crust and that the magnetic field inside the moon loses much of its energy via dissipation due to turbulence in the mantle and crust.

"How exactly the magnetic spots were formed still requires more research. But now it is clear that at some point in time an internal magnetic field of the Moon had to be present for this to happen," says Yuri Shprits, Professor at the University of Potsdam and head of the Magnetospheric Physics Section at GFZ-Potsdam. "In addition, this study can help us to better understand the nature of the dynamo-generated magnetic field and the dynamo process on Earth, the outer planets, and exoplanets."

BlueTides simulations show James Webb telescopes will reveal hidden galaxies

The blinding glare of quasars can be overcome

Two new studies from the University of Melbourne will help the largest, most powerful, and complex space telescope ever built to uncover galaxies never before seen by humanity.

The papers are published in The Astrophysical Journal and the Monthly Notices of the Royal Astronomical Society and show that NASA's James Webb Space Telescope, scheduled for launch late next year, will reveal hidden galaxies.

Powerful lights called 'quasars' are the brightest objects in the universe. Powered by supermassive black holes up to a trillion times the mass of our Sun, they outshine entire galaxies of billions of stars. An artist impression of the James Webb Space Telescope, fully deployed.{module INSIDE STORY}

Simulations led by Science Ph.D. candidate, Madeline Marshall, show that while even NASA's Hubble Space Telescope can't see galaxies currently hidden by these quasars, the James Webb Telescope will be able to get past the glare.

"Webb will open up the opportunity to observe these very distant host galaxies for the first time," said Ms. Marshall, who conducted her research at the ARC Centre of Excellence in All Sky Astrophysics in 3 Dimensions (ASTRO 3D).

"That can help us answer questions like: How can black holes grow so big so fast? Is there a relationship between the mass of the galaxy and the mass of the black hole, like we see in the nearby universe?"

Although quasars are known to reside at the centers of galaxies, it has been difficult to tell what those galaxies are like and how they compare to galaxies without quasars.

"Ultimately, Webb's observations should provide new insights into these extreme systems," said ASTRO 3D co-author Stuart Wyithe of the University of Melbourne.

"The data it gathers will help us understand how a black hole could grow to weigh a billion times as much as our Sun in just a billion years. These big black holes shouldn't exist so early because there hasn't been enough time for them to grow so massive."

The University of Melbourne team collaborated with researchers from the US, China, Germany, and The Netherlands to use the Hubble Space Telescope to try to observe these galaxies.

They then used a state-of-the-art supercomputer simulation called BlueTides, which was developed by a team led by ASTRO 3D distinguished visitor, Tiziana Di Matteo, from Carnegie Mellon University in Pittsburgh, Pennsylvania, US.

"BlueTides is designed to study the formation and evolution of galaxies and quasars in the first billion years of the universe's history," said Yueying Ni of Carnegie Mellon University, who ran the BlueTides simulation.

"Its large cosmic volume and high spatial resolution enable us to study those rare quasar hosts on a statistical basis."

The team used these simulations to determine what Webb's cameras would see if the observatory studied these distant systems. They found that distinguishing the host galaxy from the quasar would be possible, although still challenging due to the galaxy's small size in the sky.

They also found that the galaxies hosting quasars tended to be smaller than average, spanning only about 1/30 the diameter of the Milky Way despite containing almost as much mass as our galaxy.

"The host galaxies are surprisingly tiny compared to the average galaxy at that point in time," said Ms. Marshall.

Penn Medicine researchers use artificial intelligence to 'redefine' Alzheimer's Disease

The team will integrate imaging, clinical, and genetic data from more than 60,000 patients in search of new biomarkers

As the search for successful Alzheimer's disease drugs remains elusive, experts believe that identifying biomarkers -- early biological signs of the disease -- could be key to solving the treatment conundrum. However, the rapid collection of data from tens of thousands of Alzheimer's patients far exceeds the scientific community's ability to make sense of it.

Now, with a $17.8 million grant from the National Institute on Aging at the National Institutes of Health, researchers in the Perelman School of Medicine at the University of Pennsylvania will collaborate with 11 research centers to determine more precise diagnostic biomarkers and drug targets for the disease, which affects nearly 50 million people worldwide. For the project, the teams will apply advanced artificial intelligence (AI) methods to integrate and find patterns in genetic, imaging, and clinical data from over 60,000 Alzheimer's patients -- representing one of the largest and most ambitious research undertakings of its kind. {module INSIDE STORY}

Penn Medicine's Christos Davatzikos, PhD, a professor of Radiology and director of the Center for Biomedical Image Computing and Analytics, and Li Shen, PhD, a professor of Informatics, will serve as two of five co-principal investigators on the five-year project.

"Brain aging and neurodegenerative diseases, among which Alzheimer's is the most frequent, are highly heterogeneous," said Davatzikos. "This is an unprecedented attempt to dissect that heterogeneity, which may help inform treatment, as well as future clinical trials."

Diversity within the Alzheimer's patient population is a crucial reason why drug trials fail, according to the Penn researchers.

"We know that there are complex patterns in the brain that we may not be able to detect visually. Similarly, there may not be a single genetic marker that puts someone at high-risk for Alzheimer's, but rather a combination of genes that may form a pattern and create a perfect storm," said Shen. "Machine learning can help to combine large datasets and tease out a complex pattern that couldn't be seen before."

That is why the project's first objective will be to find a relationship between the three modalities (genes, imaging, and clinical symptoms), in order to identify the patterns that predict Alzheimer's diagnosis and progression -- and to distinguish between several subtypes of the disease.

"We want to redefine the term 'Alzheimer's disease.' The truth is that a treatment that works for one set of patients, may not work for another," Davatzikos said.

The investigators will then use those findings to build a predictive model of cognitive decline and Alzheimer's disease progression, which can be used to steer treatment for future patients.

This undertaking will also utilize data from the Alzheimer's Disease Sequencing Project, an NIH-funded effort led by Gerard Schellenberg, PhD, and Li-San Wang, PhD, at Penn, along with colleagues from 40 research institutions. That project aims to identify new genomic variants that contribute to -- as well as ones that protect against -- developing Alzheimer's.