A new map of dark matter in the local universe reveals several previously undiscovered filamentary structures connecting galaxies. The map, developed using machine learning by an international team including a Penn State astrophysicist, could enable studies about the nature of dark matter as well as about the history and future of our local universe.

Dark matter is an elusive substance that makes up 80% of the universe. It also provides the skeleton for what cosmologists call the cosmic web, the large-scale structure of the universe that, due to its gravitational influence, dictates the motion of galaxies and other cosmic material. However, the distribution of local dark matter is currently unknown because it cannot be measured directly. Researchers must instead infer its distribution based on its gravitational influence on other objects in the universe, like galaxies. 

"Ironically, it's easier to study the distribution of dark matter much further away because it reflects the very distant past, which is much less complex," said Donghui Jeong, associate professor of astronomy and astrophysics at Penn State and a corresponding author of the study. "Over time, as the large-scale structure of the universe has grown, the complexity of the universe has increased, so it is inherently harder to make measurements about dark matter locally."

Previous attempts to map the cosmic web started with a model of the early universe and then simulated the evolution of the model over billions of years. However, this method is computationally intensive and so far has not been able to produce results detailed enough to see the local universe. In the new study, the researchers took a completely different approach, using machine learning to build a model that uses information about the distribution and motion of galaxies to predict the distribution of dark matter.

The researchers built and trained their model using a large set of galaxy simulations, called Illustris-TNG, which includes galaxies, gasses, other visible matter, as well as dark matter. The team specifically selected simulated galaxies comparable to those in the Milky Way and ultimately identified which properties of galaxies are needed to predict the dark matter distribution.

"When given certain information, the model can essentially fill in the gaps based on what it has looked at before," said Jeong. "The map from our models doesn't perfectly fit the simulation data, but we can still reconstruct very detailed structures. We found that including the motion of galaxies--their radial peculiar velocities--in addition to their distribution drastically enhanced the quality of the map and allowed us to see these details."

The research team then applied their model to real data from the local universe from the Cosmicflow-3 galaxy catalog. The catalog contains comprehensive data about the distribution and movement of more than 17 thousand galaxies in the vicinity of the Milky Way--within 200 megaparsecs. The resulting map of the local cosmic web is published in a paper appearing online on May 26 in The Astrophysical Journal.

The map successively reproduced known prominent structures in the local universe, including the "local sheet"--a region of space containing the Milky Way, nearby galaxies in the "local group," and galaxies in the Virgo cluster--and the "local void"--a relatively empty region of space next to the local group. Additionally, it identified several new structures that require further investigation, including smaller filamentary structures that connect galaxies.

"Having a local map of the cosmic web opens up a new chapter of the cosmological study," said Jeong. "We can study how the distribution of dark matter relates to other emission data, which will help us understand the nature of dark matter. And we can study these filamentary structures directly, these hidden bridges between galaxies."

For example, it has been suggested that the Milky Way and Andromeda galaxies may be slowly moving toward each other, but whether they may collide in many billions of years remains unclear. Studying the dark matter filaments connecting the two galaxies could provide important insights into their future.

"Because dark matter dominates the dynamics of the universe, it basically determines our fate," said Jeong. "So we can ask a [super]computer to evolve the map for billions of years to see what will happen in the local universe. And we can evolve the model back in time to understand the history of our cosmic neighborhood."

The researchers believe they can improve the accuracy of their map by adding more galaxies. Planned astronomical surveys, for example using the James Web Space Telescope, could allow them to add faint or small galaxies that have yet to be observed and galaxies that are further away.

Chronic kidney disease (CKD) is caused by diabetes and hypertension. In 2017, the global prevalence of CKD was 9.1 percent, which is approximately 700 million cases. Chronic kidney damage is assessed by scoring the amount of interstitial fibrosis and tubular atrophy (IFTA) in a renal biopsy sample. Although image digitization and morphometric (measuring external shapes and dimensions) techniques can better quantify the extent of histologic damage, a more widely applicable way to stratify kidney disease severity is needed.

Now, researchers from Boston University School of Medicine (BUSM) have developed a novel Artificial Intelligence (AI) tool to predict the grade of IFTA, a known structural correlate of progressive and chronic kidney disease.

"Having a computer model that can mimic an expert pathologist's workflow and assess disease grade is an exciting idea because this technology has the potential to increase efficiency in clinical practices," explained corresponding author Vijaya B. Kolachalama, Ph.D., assistant professor of medicine at BUSM.

Typical workflow by the pathologist on the microscope involves manual operations such as panning as well as zooming in and out of specific regions on the slide to evaluate various aspects of the pathology. In the 'zoom out' assessment, pathologists review the entire slide and perform a 'global' evaluation of the kidney core. In the 'zoom in' assessment, they perform in-depth, microscopic evaluation of 'local' pathology in the regions of interest.

An international team of five practicing nephropathologists independently determined IFTA scores on the same set of digitized human kidney biopsies using web-based software (PixelView, deepPath Inc.). Their average scores were taken as a reference estimate to build the deep learning model. To emulate the nephropathologist's approach to grading the biopsy slides under a microscope, the researchers used AI to incorporate patterns and features from sub-regions (or patches) of the digitized kidney biopsy image as well as the entire (global) digitized image to quantify the extent of IFTA. Through this combination of patch-level and global-level data, a deep learning model was designed to accurately predict IFTA grade.

When validated, Kolachalama believes AI models that can automatically score the extent of chronic damage in the kidney can serve as second opinion tools in clinical practices. "Eventually, it may be possible to use this algorithm to study other organ-specific pathologies focused on evaluating fibrosis. Such methods may hold the potential to give more reproducible IFTA readings than readings by nephropathologists," he adds.

Radio telescope images enable a new way to study magnetic fields in galaxy clusters millions of light-years away

For the first time, researchers have observed plasma jets interacting with magnetic fields in a massive galaxy cluster 600 million light-years away, thanks to the help of radio telescopes and supercomputer simulations. The findings can help clarify how such galaxy clusters evolve.

Galaxy clusters can contain up to thousands of galaxies bound together by gravity. Abell 3376 is a huge cluster forming as a result of a violent collision between two sub-clusters of galaxies. Very little is known about the magnetic fields that exist within this and similar galaxy clusters.

"It is generally difficult to directly examine the structure of intracluster magnetic fields," says Nagoya University astrophysicist Tsutomu Takeuchi, who was involved in the research. "Our results clearly demonstrate how long-wavelength radio observations can help explore this interaction." 

An international team of scientists has been using the MeerKAT radio telescope in the Northern Cape of South Africa to learn more about Abell 3376's huge magnetic fields. One of the telescope's very high-resolution images revealed something unexpected: plasma jets emitted by a supermassive black hole in the cluster bend to form a unique T-shape as they extend outwards for distances as far as 326,156 light-years away. The black hole is in galaxy MRC 0600-399, which is near the centre of Abell 3376.

The team combined their MeerKAT radio telescope data with X-ray data from the European Space Agency's space telescope XXM-Newton to find that the plasma jet bend occurs at the boundary of the subcluster in which MRC 0600-399 exists.

"This told us that the plasma jets from MRC 0600-399 were interacting with something in the heated gas, called the intracluster medium, that exists between the galaxies within Abell 3376," explains Takeuchi.

To figure out what was happening, the team conducted 3D 'magnetohydrodynamic' simulations using one of the world's most powerful supercomputer, ATERUI II, located at the National Astronomical Observatory of Japan.

The simulations showed that the jet streams emitted by MRC 0600-399's black hole eventually reach and interact with magnetic fields at the border of the galaxy subcluster. The jet stream compresses the magnetic field lines and moves along them, forming the characteristic T-shape.

"This is the first discovery of an interaction between cluster galaxy plasma jets and intracluster magnetic fields," says Takeuchi.

An international team has just begun construction of what is planned to be the world's largest radio telescope, called the Square Kilometre Array (SKA).

"New facilities like the SKA are expected to reveal the roles and origins of cosmic magnetism and even to help us understand how the universe evolved," says Takeuchi. "Our study is a good example of the power of radio observation, one of the last frontiers in astronomy."

More than 90 years ago, astronomer Edwin Hubble observed the first hint of the rate at which the universe expands, called the Hubble constant.

Almost immediately, astronomers began arguing about the actual value of this constant, and over time, realized that there was a discrepancy in this number between early universe observations and late universe observations.

Early in the universe's existence, the light moved through plasma--there were no stars yet--and from oscillations similar to sound waves created by this, scientists deduced that the Hubble constant was about 67. This means the universe expands about 67 kilometers per second faster every 3.26 million light-years. 

But this observation differs when scientists look at the universe's later life after stars were born and galaxies formed. The gravity of these objects causes what's called gravitational lensing, which distorts light between a distant source and its observer.

Other phenomena in this late universe include extreme explosions and events related to the end of a star's life. Based on these later life observations, scientists calculated a different value, around 74. This discrepancy is called the Hubble tension.

Now, an international team including a University of Michigan physicist has analyzed a database of more than 1,000 supernovae explosions, supporting the idea that the Hubble constant might not actually be constant.

Instead, it may change based on the expansion of the universe, growing as the universe expands. This explanation likely requires new physics to explain the increasing rate of expansion, such as a modified version of Einstein's gravity.

The team's results are published in the Astrophysical Journal.

"The point is that there seems to be a tension between the larger values for late universe observations and lower values for early universe observation," said Enrico Rinaldi, a research fellow in the U-M Department of Physics. "The question we asked in this paper is: What if the Hubble constant is not constant? What if it actually changes?"

The researchers used a dataset of supernovae--spectacular explosions that mark the final stage of a star's life. When they shine, they emit a specific type of light. Specifically, the researchers were looking at Type Ia supernovae.

These types of supernovae stars were used to discover that the universe was expanding and accelerating, Rinaldi said, and they are known as "standard candles," like a series of lighthouses with the same lightbulb. If scientists know their luminosity, they can calculate their distance by observing their intensity in the sky.

Next, the astronomers use what's called the "redshift" to calculate how the universe's rate of expansion might have increased over time. Redshift is the name of the phenomenon that occurs when light stretches as the universe expands.

The essence of Hubble's original observation is that the further away from the observer, the more wavelength becomes lengthened--like you tacked a Slinky to a wall and walked away from it, holding one end in your hands. Redshift and distance are related.

In Rinaldi's team's study, each bin of stars has a fixed reference value of redshift. By comparing the redshift of each bin of stars, the researchers can extract the Hubble constant for each of the different bins.

In their analysis, the researchers separated these stars based on intervals of redshift. They placed the stars at one interval of distance in one "bin," than an equal number of stars at the next interval of distance in another bin, and so on. The closer the bin to Earth, the younger the stars are.

"If it's a constant, then it should not be different when we extract it from bins of different distances. But our main result is that it actually changes with distance," Rinaldi said. "The tension of the Hubble constant can be explained by some intrinsic dependence of this constant on the distance of the objects that you use."

Additionally, the researchers found that their analysis of the Hubble constant changing with redshift allows them to smoothly "connect" the value of constant from the early universe probes and the value from the late universe probes, Rinaldi said.

"The extracted parameters are still compatible with the standard cosmological understanding that we have," he said. "But this time they just shift a little bit as we change the distance, and this small shift is enough to explain why we have this tension."

The researchers say there are several possible explanations for this apparent change in the Hubble constant--one being the possibility of observational biases in the data sample. To help correct for potential biases, astronomers are using Hyper Suprime-Cam on the Subaru Telescope to observe fainter supernovae over a wide area. Data from this instrument will increase the sample of observed supernovae from remote regions and reduce the uncertainty in the data.