A new method could improve cosmology research by analyzing supernovae together with the galaxies that host them.
An international collaboration led by scientists at the Institute of Cosmos Sciences of the University of Barcelona (ICCUB) has created a new approach that may sharpen what researchers can learn about how the Universe expands and what dark energy is.
The research, published in Nature Astronomy, introduces a framework called CIGaRS. It is designed to pull more information from Type Ia supernovae, exploding stars that are central to cosmology, mainly by using imaging data rather than relying on expensive spectroscopic observations.
The method could help scientists take full advantage of the enormous datasets expected from upcoming astronomical surveys, especially those from the Vera C. Rubin Observatory.
Why supernovae are important for understanding the Universe
Type Ia supernovae occur when white dwarf stars explode. Because these explosions usually have nearly the same true brightness, astronomers treat them as “standard candles.” By comparing how bright they should be with how bright they appear from Earth, researchers can estimate distances across the cosmos.
This method played a crucial role in revealing that the Universe’s expansion is speeding up, an effect linked to dark energy, one of the deepest unsolved questions in physics. But there is an important complication: Type Ia supernovae are not perfectly identical.
The problem: supernovae are affected by their environments
During the past 20 years, astronomers have found that the brightness of Type Ia supernovae is subtly influenced by the galaxies where they occur. For instance, supernovae in older or more massive galaxies can appear slightly different from those in younger or smaller galaxies.
Until now, researchers have typically corrected for these effects using relatively simple approximations. Those shortcuts may limit how precisely scientists can use supernovae to measure cosmic distances.
A unified solution: comprehensive models
The new work addresses this challenge by modeling many connected factors together: the supernova explosions, their host galaxies, dust that dims and reddens their light, how often supernovae occur across cosmic history, and the expansion of the Universe itself.
Rather than treating each element separately, the team created one self-consistent model that connects them through both physical and statistical relationships.
“A powerful way of modeling the Universe is to simulate it ab initio in the computer using Bayesian inference,” says Raúl Jiménez (ICREA-ICCUB), co-author of the study. “This provides a way to vary all possible parameters at the same time to predict what Universe we live in. Furthermore, by having this capacity, one can look into possible ‘unknown unknown’ systematics to understand their effect. The impact of these systematics in our inference is arguably the most important missing ingredient in current approaches to model the Universe.”
Artificial intelligence and cosmology
To make this broad modeling strategy practical, the researchers used a modern approach called simulation-based inference.
The process begins with scientists creating many simulated universes based on physical models. A neural network (a type of artificial intelligence) then learns how the simulated observations connect to the underlying physical parameters. Once trained, the system can use real astronomical data to infer those parameters directly.
This makes it possible to analyze tens of thousands of supernovae together, a scale that traditional techniques could not realistically handle.
A key result: precise distances without spectroscopy
One major finding is that the method can accurately estimate galaxy distances, known as redshifts, using images alone.
Redshift describes how much a galaxy’s light has been stretched by the expansion of the Universe. It helps astronomers determine both how distant a galaxy is and how far back in time we are seeing it.
The new approach reaches a level of precision similar to spectroscopic measurements, but without requiring spectra. That matters because future sky surveys will identify millions of possible supernovae, while only a small share can be followed up with spectroscopy.
Preparation for the Rubin Observatory era
The Vera C. Rubin Observatory, now being built in Chile, will soon launch a 10-year survey of the sky. It is expected to detect an extraordinary number of supernovae, and roughly 99% of them will be observed only photometrically, meaning through images taken in different colors.
The CIGaRS framework is designed specifically for this kind of data-rich environment.
“Unlike other frameworks, which require analytic simplifications, our no-compromise end-to-end simulation-based inference approach is uniquely capable of extracting the full cosmological and astrophysical information from the Rubin Observatory’s hard-earned data, while avoiding the pitfalls of selection and modeling biases,” says Konstantin Karchev (ICCUB-SISSA Trieste), lead author of the study.
Beyond cosmology: discovering how stars explode
Beyond improving dark energy measurements, the study may also help researchers better understand how Type Ia supernovae form and when they occur. By reconstructing how supernova rates depend on the ages of stars in galaxies, the model offers a way to investigate long-standing questions about the stellar systems that produce these explosions.
The results suggest that combining physics-based modeling with artificial intelligence can address major weaknesses in current cosmological analyses. According to the authors, this method could improve cosmological constraints by as much as a factor of four compared with traditional approaches that depend only on a smaller group of spectroscopically observed supernovae.
As the Rubin Observatory prepares to reshape astronomy, tools such as CIGaRS could help researchers interpret its data more completely and better understand the Universe those observations reveal.
Reference: “CIGaRS I: combined simulation-based inference from type Ia supernovae and host photometry” by Konstantin Karchev, Roberto Trotta and Raúl Jiménez, 6 May 2026, Nature Astronomy.
DOI: 10.1038/s41550-026-02842-5
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