For about fifty years, scientists have puzzled over another related mystery. Most large galaxies near our own, aside from Andromeda, appear to be moving away from us rather than being pulled inward by gravity. This seems surprising because these galaxies reside near the Local Group (the Milky Way, the Andromeda Galaxy and dozens of smaller galaxies), whose combined mass should exert a noticeable gravitational influence.

A Giant Cosmic Sheet Around the Local Group

An international research team led by PhD graduate Ewoud Wempe of the Kapteyn Institute in Groningen believes it has found the explanation. Using advanced computer simulations, the researchers discovered that the matter surrounding the Local Group is arranged in a broad, flattened structure that stretches tens of millions of light-years across. This structure includes not only ordinary matter but also the invisible dark matter that surrounds galaxies. Above and below this flattened region lie enormous empty areas known as cosmic voids.

The simulations show that this arrangement of matter can accurately reproduce both the positions and speeds of the galaxies observed around us. In other words, the computer model successfully recreates the same patterns astronomers see in the real universe.

Creating a Virtual Twin of Our Cosmic Neighborhood

To build their model, the scientists began with conditions from the early universe. They used measurements of the cosmic microwave background to estimate how matter was distributed shortly after the Big Bang. A powerful computer then evolved this early universe forward in time, eventually producing a system that matches the present day Local Group.

The resulting simulations replicate the masses, locations, and motions of the Milky Way and Andromeda, as well as the positions and velocities of 31 galaxies just outside the Local Group. Because the model so closely resembles our surroundings, researchers describe it as a “virtual twin” of our cosmic environment.

When the model includes the flat distribution of matter, the surrounding galaxies move away from us at speeds similar to those actually observed. Despite the gravitational pull of the Local Group, galaxies within the plane are influenced by additional mass spread throughout that same plane. This distant mass counterbalances the Local Group’s gravity. Meanwhile, regions outside the plane contain very few galaxies, which explains why we do not see objects falling toward us from those directions.

A Longstanding Puzzle Finally Explained

According to lead researcher Ewoud Wempe, the study represents the first detailed attempt to determine the distribution and motion of dark matter in the area around the Milky Way and Andromeda. “We are exploring all possible local configurations of the early universe that ultimately could lead to the Local Group. It is great that we now have a model that is consistent with the current cosmological model on the one hand, and with the dynamics of our local environment on the other.”

Astronomer Amina Helmi also welcomed the findings, noting that the problem has challenged researchers for decades. “I am excited to see that, based purely on the motions of galaxies, we can determine a mass distribution that corresponds to the positions of galaxies within and just outside the Local Group.”

The finding could open new paths for designing technologies that capture sunlight more efficiently and convert it into electricity.

In laboratory experiments tracking events lasting just 18 femtoseconds — less than 20 quadrillionths of a second — researchers at the University of Cambridge observed electric charge separating during a single molecular vibration.

“We deliberately designed a system that, according to conventional theory, should not have transferred charge this fast,” said Dr. Pratyush Ghosh, Research Fellow, at St John’s College, Cambridge, and first author of the study. “By conventional design rules, this system should have been slow and that’s what makes the result so striking.

“Instead of drifting randomly, the electron is launched in one coherent burst. The vibration acts like a molecular catapult. The vibrations don’t just accompany the process, they actively drive it.”

Watching Electrons Move on the Timescale of Atoms

A femtosecond is one quadrillionth of a second — one second holds about eight times more femtoseconds than all the hours that have passed since the universe began. At this incredibly small timescale, atoms inside molecules are constantly vibrating.

The researchers observed electrons moving between materials at essentially the same pace as those atomic motions. As Ghosh explained, “We’re effectively watching electrons migrate on the same clock as the atoms themselves.”

The research, published in Nature Communications March 5, 2026, challenges long standing design assumptions in solar energy science. Until now, scientists generally believed that ultrafast charge transfer required large energy differences between materials and strong electronic coupling. Those conditions can reduce efficiency by limiting voltage and increasing energy loss.

How Light Creates Energy in Solar Materials

When light strikes many carbon based materials, it creates a tightly bound packet of energy called an exciton — a paired electron and hole. For devices such as solar cells, photodetectors and photocatalytic systems to function effectively, this pair must separate quickly into free charges.

The faster the split occurs, the less energy is wasted. This ultrafast separation plays a critical role in determining how efficiently solar panels and other light harvesting technologies convert sunlight into usable power.

To investigate whether this trade off was unavoidable, the Cambridge researchers intentionally created what they expected to be a poorly performing system. They placed a polymer donor next to a non fullerene acceptor with almost no energy difference and only weak interaction — conditions that should have significantly slowed charge transfer.

Instead, the electron crossed the interface in just 18 femtoseconds. That speed is faster than many previously studied organic systems and matches the natural rhythm of atomic motion. “Seeing it happen on this timescale within a single molecular vibration is extraordinary,” said Dr. Ghosh.

Molecular Vibrations Drive Ultrafast Electron Motion

Ultrafast laser experiments helped reveal the mechanism behind this unexpected result. When the polymer absorbs light, it begins vibrating in specific high frequency patterns.

These vibrations mix electronic states and effectively push the electron across the boundary, creating a directional, ballistic motion instead of slow and random diffusion.

Once the electron reaches the acceptor molecule, it sets off a new coherent vibration. This distinctive signal is rarely observed in organic materials and indicates how quickly the transfer occurs. “That coherent vibration is a clear fingerprint of how fast and how cleanly the transfer occurs.

“Our results show that the ultimate speed of charge separation isn’t determined only by static electronic structure,” said Dr. Ghosh. “It depends on how molecules vibrate. That gives us a new design principle. In a way, this gives us a new rulebook. Instead of fighting molecular vibrations, we can learn how to use the right ones.”

Implications for Solar Energy and Light Harvesting

The discovery suggests a new strategy for designing more efficient light harvesting technologies. Ultrafast charge separation is fundamental to systems such as organic solar cells, photodetectors and photocatalytic devices that can produce clean hydrogen fuel. Similar processes also occur naturally during photosynthesis.

Professor Akshay Rao, Professor of Physics at the Cavendish Laboratory and former St John’s College Research Associate, who was a co author of the study, said: “Instead of trying to suppress molecular motion, we can now design materials that use it — turning vibrations from a limitation into a tool.”

The project involved scientists from the Cavendish Laboratory and the Yusuf Hamied Department of Chemistry at the University of Cambridge, including Dr. Rakesh Arul, St John’s College Research Fellow. Collaborators in Italy, Sweden, the United States, Poland and Belgium also contributed to the research.

The study, partly funded by the National Institutes of Health, was published March 4 in Science Translational Medicine. It marks the first time that this type of DNA fragmentation analysis, known as fragmentome technology, has been systematically applied to detecting chronic diseases unrelated to cancer. Previously, the approach had mainly been investigated as a method for finding cancer.

Genome Wide DNA Fragment Patterns Reveal Disease Signals

Liquid biopsies that measure cfDNA have already shown promise for identifying cancer. However, scientists have not widely explored their potential for diagnosing other illnesses. In this new research, investigators performed whole genome sequencing on cfDNA samples from 1,576 individuals with liver disease and additional medical conditions. By examining DNA fragments across the entire genome, they searched for patterns that might signal disease.

The team analyzed both the size of DNA fragments and their distribution throughout the genome, including repetitive DNA regions that have rarely been studied. Each analysis included about 40 million fragments spanning thousands of genomic regions, producing an enormous dataset compared with most liquid biopsy tests.

Machine learning algorithms processed this information to identify fragmentation patterns linked to disease. Using these patterns, researchers created a classification system that detected early liver disease, advanced fibrosis and cirrhosis with high sensitivity.

“This builds directly on our earlier fragmentome work in cancer, but now using AI and genome-wide fragmentation profiles of cell-free DNA to focus on chronic diseases,” says Victor Velculescu, M.D., Ph.D., co-director of the cancer genetics and epigenetics program at the Johns Hopkins Kimmel Cancer Center and co-senior author of the study. “For many of these illnesses, early detection could make a profound difference, and liver fibrosis and cirrhosis are important examples. Liver fibrosis is reversible in early its stages, but if left undetected, it can progress to cirrhosis and ultimately increase the risk of liver cancer.”

Why DNA Fragment Analysis Is Different

Unlike many liquid biopsy methods that search for specific cancer related gene mutations, the fragmentome approach focuses on how DNA fragments are cut, packaged and distributed throughout the genome. According to the researchers, this broader view makes the method applicable to conditions beyond cancer, including diseases that can eventually raise cancer risk. The study was also co led by Robert Scharpf, Ph.D., professor of oncology, and Jill Phallen, Ph.D., assistant professor of oncology.

“The fact that we are not looking for individual mutations is what makes this study so powerful,” says first author Akshaya Annapragada, an M.D./Ph.D. student in the Velculescu lab. “We are analyzing the entire fragmentome, which contains a tremendous amount of information about a person’s physiologic state. The scale of these data, coupled with machine learning, enables development of specific classifiers for many different health conditions.”

Early Detection Could Benefit Millions at Risk

Velculescu notes that roughly 100 million people in the United States have liver conditions that increase their risk of cirrhosis and liver cancer. Current blood based tests for fibrosis often lack sensitivity, especially in early stages of disease. Standard blood markers typically fail to detect early fibrosis and identify cirrhosis only about half the time. Imaging techniques such as specialized ultrasound or magnetic resonance scans can help, but these tools require equipment that is not always available.

“Many individuals at risk don’t know they have liver disease,” Velculescu says. “If we can intervene earlier — before fibrosis progresses to cirrhosis or cancer — the impact could be substantial.”

He adds that identifying these precursor conditions early may allow doctors to treat underlying diseases sooner and potentially prevent cancer from developing.

Study Origins and the Fragmentome Comorbidity Index

The research grew out of a 2023 Cancer Discovery study led by Velculescu that focused on the fragmentome of liver cancer. While studying patients with liver tumors, the scientists noticed that some individuals with fibrosis or cirrhosis showed mostly normal fragmentation profiles but contained subtle DNA signals linked to disease. This observation prompted the team to examine the fragmentome patterns associated specifically with liver fibrosis and cirrhosis.

In another analysis involving 570 people with suspected serious illness, researchers created a fragmentation comorbidity index. This measure distinguished individuals with high and low Charlson Comorbidity Index scores, a widely used metric that estimates how additional health conditions affect a person’s risk of death. The fragmentome based index predicted overall survival independently and in some cases proved more specific than traditional inflammatory markers. Certain fragmentation signatures also appeared to be associated with poorer clinical outcomes.

“The fragmentome can serve as a foundation for building different classifiers for different diseases, and importantly, these classifiers are disease-specific and do not cross-react,” Annapragada says. “A liver fibrosis classifier is distinct from a cancer classifier. This is a unique, disease-specific test built from the same underlying platform.”

Potential to Detect Other Chronic Diseases

The study also included people at elevated risk for a range of medical conditions. Researchers observed fragmentome signals linked to cardiovascular, inflammatory and neurodegenerative disorders. However, the study population did not include enough cases to build separate disease classifiers for each of these conditions. Instead, the findings suggest that the technology may eventually have wider medical applications, which researchers plan to investigate in future work.

The liver fibrosis assay described in the study remains a prototype and has not yet been introduced as a clinical test. The team’s next steps involve refining and validating the classifier for liver disease and exploring fragmentome signatures connected to other chronic illnesses.

Researchers and Funding

Along with Velculescu, Annapragada, Scharpf and Phallen, the research team included Zachariah Foda, Hope Orjuela, Carter Norton, Shashikant Koul, Noushin Niknafs, Sarah Short, Keerti Boyapati, Adrianna Bartolomucci, Dimitrios Mathios, Michael Noe, Chris Cherry, Jacob Carey, Alessandro Leal, Bryan Chesnick, Nic Dracopoli, Jamie Medina, Nicholas Vulpescu, Daniel Bruhm, Sarah Bacus, Vilmos Adleff, Amy Kim, Stephen Baylin, Gregory Kirk, Andrei Sorop, Razvan Iacob, Speranta Iacob, Liana Gheorghe, Simona Dima, Katherine McGlynn, Manuel Ramirez-Zea, Claus Feltoft, Julia Johansen and John Groopman.

Funding for the research came in part from the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, SU2C in-Time Lung Cancer Interception Dream Team Grant, Stand Up to Cancer-Dutch Cancer Society International Translational Cancer Research Dream Team Grant, the Gray Foundation, The Honorable Tina Brozman Foundation, the Commonwealth Foundation, the Mark Foundation for Cancer Research, the Danaher Foundation and ARCS Metro Washington Chapter, the Family of Dan Y. Zhang AACR Scholar in Training Award, the Cole Foundation and National Institutes of Health grants CA121113, CA006973, CA233259, CA062924, CA271896, T32GM136577, T32GM148383 and DA036297.