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Researchers have developed a new analysis technique to extract significantly more information from galaxy surveys, delivering the most precise weak-lensing-only measurements yet of the Universe’s dark matter distribution.

Dark matter and dark energy account for around 95% of the Universe, yet their nature remains one of the greatest mysteries in modern science. Understanding them requires measuring how matter is distributed across the cosmos and how cosmic structures have evolved over billions of years.

One of the most powerful tools for doing this is weak gravitational lensing—the subtle distortion of distant galaxies caused by the gravity of matter lying between those galaxies and Earth. Modern sky surveys can measure these distortions for hundreds of millions of galaxies, but extracting precise cosmological information from such vast datasets remains a major statistical challenge.

Now, researchers at 911½ñÈÕºÚÁÏ and University College London have shown that combining physics-based analysis with machine learning can significantly improve what can be learned from these observations.

Using data from the (DES), the team developed a hybrid analysis framework that combines established cosmological statistics with machine-learning-derived summaries of the data, while rigorously accounting for uncertainties throughout the analysis.

Applied to DES Year 3 weak gravitational lensing observations, the method extracts around three times more cosmological information than traditional approaches. It delivers the most precise weak-lensing-only measurements yet of both the matter density of the Universe and the strength of cosmic clustering.

The results are also consistent with measurements from the Planck satellite, which mapped the relic radiation left behind by the Big Bang, fully resolving a long-standing tension between weak-lensing observations and measurements of the early Universe.

The findings have been submitted to Monthly Notices of the Royal Astronomical Society, and a preprint is available at .

Reading the Universe through distorted galaxies

Weak gravitational lensing provides a unique way to map the invisible matter that fills the Universe and drives the growth of cosmic structure.

As light from distant galaxies travels towards Earth, its path is subtly bent by the gravitational pull of matter along the way. These tiny distortions alter the apparent shapes of galaxies by only a few percent, but when measured across tens or hundreds of millions of galaxies they reveal how matter is distributed throughout the cosmos.

The Dark Energy Survey measured the shapes of approximately 100 million galaxies across an area of sky roughly 25,000 times larger than the full Moon, creating one of the most detailed maps ever made of the large-scale structure of the Universe.

Traditionally, weak-lensing analyses compress these enormous datasets into so-called two-point statistics, which measure how galaxy distortions are correlated across the sky. These methods are robust and have underpinned modern cosmology for decades. However, they do not capture all of the information present in the data, particularly on smaller scales where the distribution of matter becomes highly complex.

The new framework is designed to recover more of that information.

Starting from familiar observables such as the weak-lensing power spectrum, the researchers augment the analysis with machine-learning-derived summary statistics learned from realistic simulations of the Universe.

Lucas Makinen, 911½ñÈÕºÚÁÏ PhD student who co-led the work and original author of the hybrid learning algorithm, said “Using ideas from information theory, we can guide neural networks towards the most informative features in weak-lensing maps. I think of it as telling the networks where to look in the data.”

This isn’t AI replacing physics. We use AI to compress information from around 100 million galaxy measurements into just seven highly informative numbers. Almost all of the relevant physics is captured in those seven quantities. Professor Alan Heavens Department of Physics

Rather than replacing physical modelling, the machine-learning component is used in a controlled and interpretable way to identify additional patterns that are sensitive to the properties of dark matter and dark energy.

Professor Alan Heavens, 911½ñÈÕºÚÁÏ’s Department of Physics, said “This isn’t AI replacing physics. We use AI to compress information from around 100 million galaxy measurements into just seven highly informative numbers. Almost all of the relevant physics is captured in those seven quantities.”

Natalia Porqueres, CEA Saclay,  added “In contrast to some earlier studies, results are in excellent and very precise agreement with measurements of the light left over from the Big Bang.”

Sharper measurements of dark matter and dark energy

Using the new framework, the researchers obtained significantly tighter constraints on key cosmological parameters, including the present-day matter density of the Universe, the strength of matter clustering and the behaviour of dark energy.

Compared with standard analyses of the same DES dataset, the method improves cosmological parameter constraints by approximately a factor of three.

One of the central challenges in cosmology is that different physical processes can produce similar observational signatures. Variations in dark matter density, matter clustering and dark energy can all affect the appearance of the Universe in related ways, making it difficult to disentangle their individual contributions.

The hybrid framework helps overcome this challenge by identifying subtle patterns in the data that allow these effects to be separated more effectively.

The work comes as cosmology enters an era of increasingly large and complex datasets. Next-generation surveys, including the European Space Agency’s Euclid mission and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, will observe billions of galaxies and generate datasets far larger and richer than those available today.

Making full use of these observations will require methods that can extract as much information as possible while remaining physically reliable and scientifically interpretable.

We taught our neural networks the physics we already know, and then trained them to find the extra information hidden beyond it. We were amazed to see the result: nearly three times more information from the same data, and our most precise measurement yet of the matter and dark energy that shape our Universe Joshua Williamson co-lead at UCL

Niall Jeffrey, UCL, said “We found that combining physical insight with machine learning dramatically improves what AI systems can learn from cosmological data. Rather than treating the algorithm as a black box, we can guide it towards the most informative features—and in doing so learn much more about dark energy.”

Joshua Williamson, co-lead at UCL,  said “We taught our neural networks the physics we already know, and then trained them to find the extra information hidden beyond it. We were amazed to see the result: nearly three times more information from the same data, and our most precise measurement yet of the matter and dark energy that shape our Universe.”

Dark matter and dark energy dominate the Universe, but much of their imprint is encoded in subtle patterns that conventional analyses struggle to capture.

By recovering information that would otherwise be left behind, the new method provides a sharper view of the dark Universe and a more powerful way to test competing theories of cosmic evolution.

As Euclid and the Rubin Observatory begin delivering unprecedented observations of billions of galaxies, techniques like this could help answer some of the most fundamental questions in physics: what dark matter is, whether dark energy changes over time, and whether our current understanding of the Universe is complete.