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Category: mapping

NOvA maps neutrino oscillations over 500 miles with 10 years of data

Neutrinos are very small, neutral subatomic particles that rarely interact with ordinary matter and are thus sometimes referred to as ghost particles. There are three known types (i.e., flavors) of neutrinos, dubbed muon, electron and tau neutrinos.

Interestingly, physicists discovered that as they travel, neutrinos can change flavor, which requires that neutrinos have a small, but not zero, mass. This phenomenon, known as neutrino oscillation, has been widely investigated in recent years, as studying it could help to infer the properties of neutrinos.

The NOvA experiment, a U.S.-based particle physics research endeavor, has been collecting data with two neutrino detectors that are far apart from each other, one located at the Fermi National Laboratory (Fermilab) in Illinois and the other at a facility in Northern Minnesota. In a recent paper, published in Physical Review Letters, the researchers involved in the NOvA experiment published some of the most precise neutrino oscillation measurements to date.

Mapping tool delivers quantitative visualisations of steric interactions

Another system the team Self on was a set of Ni–phosphine complexes, where steric interactions often limit the accessibility of the phosphine ligand’s lone pair. The researchers found that their results correlated well with the traditional method to study this – Tolman’s angles – but also showed that the interaction between the nickel centre and the ligand is smaller than Tolman analysis suggests.

These insights could prove useful for chemists. As Paton notes, ‘you could imagine comparing many different ligands in a library with this tool and using it to, for example, characterise or understand performance, maybe even ligand design… ultimately, that will allow us to think about how we might design better and more efficient systems and reactions.’

Hénon highlights that Self also works inside molecules, not just between them. ‘Previous methods were designed for two separate molecules approaching each other. But what about rotation barriers within a single molecule. That’s intramolecular steric repulsion, and Self handles it naturally. Other methods struggle badly with this. This opens new perspectives.’

⏱️ The Brilliant Laziness of Being Human: Why Your Brain Refuses to Plan Ahead (And That’s Actually Perfect)

“Useless blocks” that you’d bump into 95% of the time but didn’t affect the outcome at all.

New MIT research reveals how humans navigate complex environments: not through exhaustive mental mapping, but through ‘just-in-time’ processing—building simplified models only as needed. This challenges decades of cognitive theory and has profound implications for AI, robotics, and understanding everyday human behaviour and memory.

New 3D method maps Paleolithic engravings at submillimeter resolution

A team of archaeologists from the Universitat Jaume I, the University of Barcelona, and the Catalan Institution for Research and Advanced Studies (ICREA) has developed a new methodology that allows for a much more detailed, precise, and objective analysis of Late Paleolithic portable art pieces. Thanks to this study, the research team was able to review several previously published pieces from Matutano Cave (Vilafamés), a reference site in the Iberian Mediterranean, with greater accuracy and demonstrate that some of the marks previously interpreted as artistic motifs are not anthropic engravings but natural surface reliefs.

Late Paleolithic art is usually characterized by very fine engravings, barely visible to the naked eye, often affected by taphonomic alterations, surface irregularities, and unclear morphologies, which complicates their identification and interpretation. This new methodology allows for a more precise analysis of the remains using photogrammetry and microtopographic analysis techniques.

The results are published in the Journal of Archaeological Science: Reports.

Scientists just mapped the brain architecture that underlies human intelligence

For decades, researchers have attempted to pinpoint the specific areas of the brain responsible for human intelligence. A new analysis suggests that general intelligence involves the coordination of the entire brain rather than the superior function of any single region. By mapping the connections within the human brain, or connectome, scientists found that distinct patterns of global communication predict cognitive ability.

The research indicates that intelligent thought relies on a system-wide architecture optimized for efficiency and flexibility. These findings were published in the journal Nature Communications.

General intelligence represents the capacity to reason, learn, and solve problems across a variety of different contexts. In the past, theories often attributed this capacity to specific networks, such as the areas in the frontal and parietal lobes involved in attention and working memory. While these regions are involved in cognitive tasks, newer perspectives suggest they are part of a larger story.

‘Discovery learning’ AI tool predicts battery cycle life with just a few days’ data

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new battery concepts. With information from just 50 cycles, the tool—developed at University of Michigan Engineering—can predict how many charge-discharge cycles the battery can undergo before its capacity drops below 90% of its design capacity.

This could save months to years of testing, depending on the conditions of cycling experiments, as well as substantial electrical power during battery prototyping and testing. The team estimates that the cycle lives of new battery designs could be predicted with just 5% of the energy and 2% of the time required by conventional testing.

“When we learn from the historical battery designs, we leverage physics-based features to construct a generalizable mapping between early-stage tests and cycle life,” said Ziyou Song, U-M assistant professor of electrical and computer engineering and corresponding author of the study in Nature. “We can minimize experimental efforts and achieve accurate prediction performance for new battery designs.”

X-rays from SLAC’s synchrotron reveal star maps in a centuries-old manuscript

Pages from the Codex Climaci Rescriptus palimpsest from the Museum of the Bible in Washington, DC, were brought to the Stanford Synchrotron Radiation Lightsource to recover erased astronomical text, especially fragments from Hipparchus’ star catalog.

Using data to reduce subjectivity in landslide susceptibility mapping

In recent years, numerous landslides on hillsides in urban and rural areas have underscored that understanding and predicting these phenomena is more than an academic curiosity—it is a human necessity. When unstable slopes give way after intense rainfall, the consequences can be devastating, with both human and material losses. These recurring tragedies led us to a simple yet powerful question: Can we build landslide susceptibility maps that are more objective, transparent, and useful for local authorities and residents?

The answer led us to compare two susceptibility analysis methods: the traditional Analytical Hierarchy Process (AHP) and its statistical version, the Gaussian AHP. After months of multidisciplinary work, we found that the Gaussian AHP, which relies on data rather than subjective judgments, better identifies critical areas in a more balanced manner and is consistent with the landslide patterns observed in the field. We share here our journey and the lessons we learned. Our findings are published in Scientific Reports.

Traditional AHP is a decision-support technique widely used in geosciences and urban planning. It relies on pairwise comparisons of factors such as slope, soil type, and distance to rivers or roads to assign relative weights based on expert opinion. One advantage is that it allows the incorporation of accumulated experience; a disadvantage is the subjectivity and the effort required when many factors are involved. In our case, we worked with 16 physical and environmental variables that influence slope instability—from terrain morphometry to land cover and proximity to rivers.

New 3D map of the sun’s magnetic interior could improve predictions of disruptive solar flares

For the first time, scientists have used satellite data to create a 3D map of the sun’s interior magnetic field, the fundamental driver of solar activity. The research, published in The Astrophysical Journal Letters, should enable more accurate predictions of solar cycles and space weather that affects satellites and power grids.

The sun is more than just a fiery hot ball of hydrogen and helium gas. It is a giant magnetic star. Beneath the surface is a magnetic layer that is responsible for everything from the dark spots we see on its face to violent flares that erupt into space. Because of the disruption caused by solar storms, we need to know what is going on inside. We can’t directly observe the interior, so to date we have relied on models that depend on simplified assumptions. But these can be inaccurate.

To get a better idea of what is going on inside the sun, researchers from India fed 30 years of daily magnetic maps from satellites (from 1996 to 2025) into a sophisticated 3D model of the solar dynamo, the physical process that generates the sun’s magnetic field. By using this real-world data, they could track how magnetic fields move deep beneath the surface, where satellites cannot penetrate.

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