A new way to read the universe could sharpen understanding of cosmic expansion and dark energy

Key Points:

• Researchers at the University of Barcelona's Institute of Cosmos Sciences have developed CIGaRS, a new framework that uses imaging and AI to extract detailed information from Type Ia supernovae, improving our understanding of cosmic expansion and dark energy.
• Type Ia supernovae serve as "standard candles" for measuring cosmic distances, but their brightness varies with host galaxy properties, complicating distance estimates; CIGaRS addresses this by modeling supernovae, host galaxies, dust effects, and cosmic expansion simultaneously in a unified, Bayesian framework.
• The approach leverages simulation-based inference and neural networks to analyze large datasets efficiently, enabling precise distance (redshift) measurements from images alone, comparable to traditional spectroscopic methods, which is critical given the vast number of supernovae expected from upcoming surveys.
• Designed for the Vera C. Rubin Observatory's 10-year sky survey, which will detect millions of supernovae mostly through photometry, CIGaRS can handle large-scale data without biases, maximizing the scientific return on dark energy and cosmological studies.
• Beyond cosmology, the method provides insights into the progenitor systems of Type Ia supernovae by reconstructing their occurrence rates relative to stellar ages, potentially improving cosmological constraints by up to four times compared to existing spectroscopic-based analyses.