Researchers led by the Institute of Space Sciences of the University of Barcelona (ICCUB) have developed a new technique that could significantly improve the way scientists study the expansion of the universe and investigate the mysterious force known as dark energy.
Published in natural astronomyThe study introduces a framework called CIGaRS that can extract far more information from type Ia supernovae, powerful stellar explosions used to measure vast cosmic distances. Unlike many current approaches, this method relies primarily on image data rather than expensive spectroscopic observations. This advancement is expected to help astronomers take full advantage of the vast datasets soon to arrive from the next generation of sky surveys, particularly those conducted by the Vera C. Rubin Observatory.
Why type Ia supernovae are important
Type Ia supernovae occur when a white dwarf star explodes. Because these explosions reach approximately the same intrinsic brightness, astronomers use them as “standard candles.” By comparing the actual brightness to the brightness seen from Earth, researchers can calculate the distance to the explosion.
These measurements played a key role in the discovery that the universe is expanding at an accelerating rate. Scientists believe that the acceleration is due to dark energy, one of the most important unsolved problems in modern physics.
However, there are important complications. Type Ia supernovae are not completely identical.
Influence of host galaxy on supernova measurements
Over the past two decades, astronomers have discovered that the brightness at which supernovae are observed is influenced by the galaxy in which they occur. Supernovae found in older or more massive galaxies can look slightly different than supernovae that occur in younger or less massive galaxies.
Researchers have typically used relatively simple correction methods to account for these differences. Although useful, these approximations can limit the accuracy of distance measurements and, in turn, the accuracy of cosmological studies.
A unified model of supernovae and the universe
The new framework addresses this challenge by modeling multiple elements simultaneously. Rather than treating each component separately, the researchers built a single integrated model that included the supernova explosion itself, its host galaxy, the light-altering dust, changes in supernova rates throughout the history of the universe, and even the expansion of the universe.
By connecting all these elements within a single statistical and physical framework, teams can capture relationships that are often overlooked when analyzing the elements individually.
“A powerful way to model the universe is to use Bayesian inference to simulate the universe from scratch in a computer,” said study co-author Raul Jimenez (ICREA-ICCUB). “This provides a way to vary all possible parameters simultaneously to predict the universe we live in. Furthermore, this ability allows us to examine possible ‘unknown, unknown’ systems and understand their effects. The influence of these systems on our inferences is perhaps the most important element missing from current approaches to modeling the universe.”
Analyzing the universe using artificial intelligence
Building such comprehensive models typically requires significant computational power. To put this approach into practice, the researchers turned to a modern technique called simulation-based inference.
The process begins with scientists generating a large number 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, which is impractical with conventional techniques.
Estimate accurate galaxy distances only from images
One of the most important findings of this study is that this framework can determine galactic distances (redshifts) with high accuracy using only image data.
Redshift measures how much a galaxy’s light has been stretched as the universe expands. It provides information about the distance to galaxies and how far back in time we have been observing them.
According to the researchers, this new method provides redshift estimates with an accuracy comparable to spectroscopic measurements, without the need for spectra. This capability is particularly important because while future surveys are expected to identify millions of supernova candidates, only a small number will realistically receive spectroscopic follow-up observations.
Rubin Observatory prepares for data deluge
The Vera C. Rubin Observatory, currently under construction in Chile, is scheduled to begin a 10-year survey of the skies in the near future. During that mission, an unprecedented number of supernovae will be discovered. About 99% of those objects are only observed photometrically. That is, it is observed through images taken in different colors rather than a detailed spectrum.
The CIGaRS framework was developed specifically with this challenge in mind.
“Unlike other frameworks that require analysis simplification, our no-compromise, end-to-end simulation-based inference approach has the unique ability to extract complete cosmological and astrophysical information from the Rubin Observatory’s hard-won data, while avoiding the pitfalls of selection and modeling bias,” said Konstantin Karchev (ICCUB-SISSA Trieste), lead author of the study.
Insights into how supernovae form
Its benefits extend beyond measuring dark energy. This framework also provides new information about the origin of type Ia supernovae themselves.
By reconstructing how supernova rates vary with the age of stars in different galaxies, the model will ultimately help scientists investigate long-standing questions about the systems that cause these explosions.
Researchers have found that combining physics-based simulations with artificial intelligence can overcome some of the limitations of current cosmological methods. They estimate that this approach has the potential to improve cosmological constraints by a factor of four compared to traditional techniques that rely only on a relatively small sample of spectroscopically observed supernovae.
As Rubin Observatory prepares to usher in a new era of astronomical discovery, tools like CIGaRS can help scientists extract the most information from their observations and gain a deeper understanding of the universe.
