This research combines galaxy simulations with machine learning to study the invisible gas surrounding galaxies. By training a neural network to interpret astronomical observations, the project creates a public tool—the Circumgalactic Dictionary—that enables previously impossible measurements, advancing our understanding of galaxy evolution and the origins of stars, planets, and life.

This research investigates whether dark energy, responsible for the universe’s accelerating expansion, evolves over time rather than remaining constant. Using galaxy distributions, supernovae, and cosmic microwave data, new statistical methods suggest evolving models may better fit observations, potentially reshaping our understanding of cosmology and the universe’s long-term fate.