Researchers led by the Institute of Cosmos Sciences of the University of Barcelona (ICCUB) have developed a new technique that could greatly improve how scientists study the expansion of the Universe and investigate the mysterious force known as dark energy.
Published in Nature Astronomy, the research introduces a framework called CIGaRS that can extract far more information from Type Ia supernovae, the powerful stellar explosions used to measure vast cosmic distances. Unlike many current approaches, the method relies mainly on imaging data rather than expensive spectroscopic observations. The advance is expected to help astronomers take full advantage of the enormous datasets that will soon arrive from next generation sky surveys, particularly those conducted by the Vera C. Rubin Observatory.
Why Type Ia Supernovae Matter
Type Ia supernovae occur when white dwarf stars explode. Because these explosions reach nearly the same intrinsic brightness, astronomers use them as "standard candles": by comparing their actual brightness with how bright they appear from Earth, researchers can calculate their distance.
These measurements played a crucial role in the discovery that the Universe is expanding at an accelerating rate. Scientists attribute that acceleration to dark energy, one of the most significant unsolved questions in modern physics.
However, there is an important complication. Type Ia supernovae are not perfectly identical.
How Host Galaxies Affect Supernova Measurements
Over the past 20 years, astronomers have found that a supernova's observed brightness is influenced by the galaxy where it occurs. Supernovae found in older or more massive galaxies can appear slightly different from those occurring in younger or less massive galaxies.
Researchers have typically accounted for these differences using relatively simple correction methods. While useful, these approximations can limit the accuracy of distance measurements and, in turn, the precision of cosmological studies.
A Unified Model of Supernovae and the Universe
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