An international team led by researchers at the Institute of Cosmos Sciences of the University of Barcelona (ICCUB) has developed a new method that could significantly improve our understanding of the expansion of the Universe and the nature of dark energy.
The study, published today in Nature Astronomy, presents a powerful framework called CIGaRS that allows scientists to extract more information from exploding stars known as Type Ia supernovae, primarily through imaging rather than costly spectroscopic observations. The results pave the way for making the most of the vast amount of data expected from the next generation of astronomical surveys, especially from the Vera C. Rubin Observatory.
Why supernovae are important for understanding the Universe
Type Ia supernovae are the explosive deaths of white dwarf stars. Since they tend to explode with almost the same intrinsic brightness, astronomers use them as "standard candles": by comparing their known true brightness with their apparent brightness from Earth, scientists can measure cosmic distances.
This technique was key to discovering that the expansion of the Universe is accelerating, a phenomenon attributed to dark energy, one of the biggest mysteries of modern physics. However, there is a catch: not all Type Ia supernovae are exactly the same.
The problem: supernovae are affected by their environments
Over the last two decades, astronomers have discovered that the brightness of these supernovae depends slightly on the galaxies in which they explode. For example, supernovae in the most massive or oldest galaxies tend to look slightly different from those in smaller or younger galaxies.
To date, these effects had been corrected using simple, approximate adjustments, which could limit how precisely we can measure the distances to these supernovae.
A unified solution: comprehensive models
The new study tackles this problem by modelling everything at once: supernova explosions, the galaxies that host them, the dust that dims and reddens their light, the frequency with which supernovae occur over cosmic time, and even the expansion of the Universe itself.
Instead of analysing each piece separately, the team built a single, self-consistent model that links all these elements physically and statistically.
"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."
Artificial intelligence and cosmology
To make this ambitious approach computationally feasible, the team used a modern set of techniques known as simulation-based inference.
In simple terms, the method works like this: first, scientists simulate many possible universes using physical models; next, a neural network (a type of artificial intelligence) learns how the simulated data relate to the underlying physical parameters, and finally the trained system can infer these parameters directly from real observations.
This allows the analysis of tens of thousands of supernovae at once, something that would be impossible with traditional methods.
