Researchers led by the Institute of Cosmos Sciences of the University of Barcelona (ICCUB) have developed a new technique that could greatly improve how scientists study the expansion of the Universe and investigate the mysterious force known as dark energy.
Published in Nature Astronomy, the research introduces a framework called CIGaRS that can extract far more information from Type Ia supernovae, the powerful stellar explosions used to measure vast cosmic distances. Unlike many current approaches, the method relies mainly on imaging data rather than expensive spectroscopic observations. The advance is expected to help astronomers take full advantage of the enormous datasets that will soon arrive from next generation sky surveys, particularly those conducted by the Vera C. Rubin Observatory.
Why Type Ia Supernovae Matter
Type Ia supernovae occur when white dwarf stars explode. Because these explosions reach nearly the same intrinsic brightness, astronomers use them as “standard candles”: by comparing their actual brightness with how bright they appear from Earth, researchers can calculate their distance.
These measurements played a crucial role in the discovery that the Universe is expanding at an accelerating rate. Scientists attribute that acceleration to dark energy, one of the most significant unsolved questions in modern physics.
However, there is an important complication. Type Ia supernovae are not perfectly identical.
How Host Galaxies Affect Supernova Measurements
Over the past 20 years, astronomers have found that a supernova’s observed brightness is influenced by the galaxy where it occurs. Supernovae found in older or more massive galaxies can appear slightly different from those occurring in younger or less massive galaxies.
Researchers have typically accounted for these differences using relatively simple correction methods. While useful, these approximations can limit the accuracy of distance measurements and, in turn, the precision of cosmological studies.
A Unified Model of Supernovae and the Universe
The new framework addresses this challenge by modeling multiple factors simultaneously. Rather than treating each component independently, the researchers built a single, integrated model that includes the supernova explosions themselves, their host galaxies, the dust that alters their light, changes in supernova rates throughout cosmic history, and even the expansion of the Universe.
By connecting all of these ingredients within one statistical and physical framework, the team can capture relationships that are often overlooked when the pieces are analyzed separately.
“A powerful way of modelling 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.”
Using Artificial Intelligence To Analyze the Cosmos
Building such a comprehensive model would normally require enormous computing power. To make the approach practical, the researchers turned to a modern technique called simulation-based inference.
The process begins with scientists generating large numbers of simulated universes based on physical models. A neural network (a type of artificial intelligence) then learns how the simulated observations relate to the physical properties that produced them. Once trained, the system can compare real astronomical observations with its simulations and determine the most likely underlying parameters.
This strategy makes it possible to analyze tens of thousands of supernovae simultaneously, a task that would be impractical using traditional techniques.
Accurate Galaxy Distances From Images Alone
One of the study’s most significant findings is that the framework can determine galaxy distances (redshifts) with high accuracy using only imaging data.
Redshift measures how much a galaxy’s light has been stretched as the Universe expands. It provides information about both the galaxy’s distance and how far back in time we are observing it.
According to the researchers, the new method delivers redshift estimates with precision comparable to spectroscopic measurements, but without requiring spectra. This capability is especially important because upcoming surveys are expected to identify millions of supernova candidates, while only a small percentage can realistically receive spectroscopic follow-up observations.
Ready for the Rubin Observatory Data Deluge
The Vera C. Rubin Observatory, currently being built in Chile, is expected to begin a decade long survey of the sky in the near future. During that mission, it will discover an unprecedented number of supernovae. Roughly 99% of those objects will be observed only photometrically, meaning through images taken in different colors rather than detailed spectra.
The CIGaRS framework was specifically developed with this challenge in mind.
“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 modelling biases,” says Konstantin Karchev (ICCUB-SISSA Trieste), lead author of the study.
Insights Into How Supernovae Form
The benefits extend beyond measuring dark energy. The framework also provides new information about the origins of Type Ia supernovae themselves.
By reconstructing how supernova occurrence rates vary with the ages of stars in different galaxies, the model helps scientists investigate long standing questions about the systems that eventually produce these explosions.
The researchers found that combining physics based simulations with artificial intelligence can overcome several limitations of current cosmological methods. They estimate that the approach could improve cosmological constraints by as much as a factor of four compared with traditional techniques that depend only on a relatively small sample of spectroscopically observed supernovae.
As the Rubin Observatory prepares to usher in a new era of astronomical discovery, tools such as CIGaRS could help scientists extract the maximum amount of information from its observations and gain a deeper understanding of the Universe.