source: sciencedaily ai: millions of exploding stars could soon reveal dark energy's secrets
level: research
astronomers have built an ai-driven tool called cigars that extracts more information from type ia supernovae, the stellar explosions used as standard candles to measure cosmic distances. unlike traditional methods that rely on expensive spectroscopy, this framework works mainly with imaging data. it models supernovae, their host galaxies, dust, and the universe's expansion together in one statistical model, capturing relationships that separate analyses miss.
the team used simulation-based inference, where a neural network learns from many simulated universes to connect observations with physical properties. this allows the system to estimate galaxy redshifts from images alone with accuracy comparable to spectroscopy. the approach can handle tens of thousands of supernovae at once, making it practical for the huge datasets expected from the vera c. rubin observatory, which will discover millions of supernovae but obtain spectra for only a small fraction.
the framework also sheds light on how type ia supernovae form by linking their rates to stellar ages in host galaxies. researchers estimate it could improve cosmological constraints by up to a factor of four compared to methods relying on small spectroscopic samples. as the rubin observatory begins its decade-long sky survey, tools like cigars could help scientists get the most out of its observations and deepen understanding of dark energy.
why it matters: this method lets astronomers use millions of supernova images to measure cosmic expansion more precisely, improving dark energy constraints without needing costly spectra.
source: sciencedaily ai: millions of exploding stars could soon reveal dark energy's secrets