A new study could help scientists study dark energy with greatly improved precision and accuracy. Scientists have discovered an approach to double the precision of estimating distances to supernova explosion —one of their tested tools for contemplating dark energy.
According to scientists, the study could be a foundation for future cosmology experiments that will test alternative explanations of dark energy.
In 1998, supernovae were used to make the startling discovery that the expansion of the universe is expanding. This acceleration—attributed to dark energy.
The discovery of dark energy relied on using a particular class of supernovae, Type Ia. Te brightness of supernova is used to determine its distance; the small remaining variations in the intrinsic maximum brightness limited the precision with which dark energy could be tested.
Despite 20 years of improvements by numerous roups, supernovae investigations of dark energy have yet remained limited by these variations.
Estimating dark energy requires comparisons of the maximum brightnesses of distant supernovae billions of light-years away with those of close by supernovae “just” 300 million light-years away. Scientists studied hundreds of such nearby supernovae in detail, in which they studied the spectrum of the supernova, recording its intensity across the wavelength range of visible light.
To measure the spectrum, they used the SuperNova Integral Field Spectrometer, installed at the University of Hawaii 2.2-meter telescope at Maunakea.
Saul Perlmutter, the Berkeley Lab senior scientist, said, “We’ve long had this idea that if the physics of the explosion of two supernovae were the same, their maximum brightnesses would be the same. Using the Nearby Supernova Factory spectra as a kind of CAT scan through the supernova explosion, we could test this idea.”
Several years ago, physicist Hannah Fakhouri, then a graduate student working with Perlmutter, made a discovery key to today’s results. Looking at a multitude of spectra taken by the SNfactory, she found that the spectra from two different supernovae looked very nearly identical in quite several instances. Among the 50 or so supernovae, some were virtually similar twins.
When the wiggly spectra of a pair of twins were superimposed to the eye, there was just a single track. The current analysis builds on this observation to model the behavior of supernovae in the period near the time of their maximum brightness.
This new study fourfold the number of supernovae used in the analysis. Due to the large set of samples, scientists choose to use machine-learning techniques to identify these twins.
They found that Type Ia supernova spectra vary in only three ways. The intrinsic brightnesses of the supernovae also depend primarily on these three observed differences, making it possible to measure supernova distances to a remarkable accuracy of about 3%.
Kyle Boone, as lead author, said, “Conventional measurement of supernova distances uses light curves—images taken in several colors as a supernova brightens and fades. Instead, we used a spectrum of each supernova. These are so much more detailed, and with machine-learning techniques,s it then became possible to discern the complex behavior that was key to measuring more accurate distances.”
Greg Aldering of the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) said, “not only is this distance measurement technique more accurate, it only requires a single spectrum, taken when a supernova is brightest and thus easiest to observe—a game changer!” Having various techniques is particularly valuable in this field where preconceptions have turned out to be wrong and the need for independent verification is high.”
- K. Boone et al. The Twins Embedding of Type Ia Supernovae. I. The Diversity of Spectra at Maximum Light, The Astrophysical Journal (2021). DOI: 10.3847/1538-4357/abec3c
- K. Boone et al. The Twins Embedding of Type Ia Supernovae. II. Improving Cosmological Distance Estimates, The Astrophysical Journal (2021). DOI: 10.3847/1538-4357/abec3b