This Darchive presents the DES cosmological constraints from the full five years of Type Ia Supernovae data (pronounced as ‘one-A’). This DES data was combined with a historical set of close supernovae from other surveys. The full paper can be found at https://arxiv.org/pdf/2401.02929 .
Today, cosmologists know that both dark energy and dark matter affect the expansion of the Universe. A key probe to understanding the expansion of the Universe, and thus more about dark energy and dark matter, is the study of Type Ia supernovae. In fact, it was the study of Type Ia supernovae that led to the discovery of the accelerated expansion of our Universe, and thus dark energy.
Figure 1: Just like a candle looks dimmer if it is far away, distant Type Ia supernovae will look dimmer than nearby ones. Credit: NASA/JPL)
What is so significant about Type Ia supernovae? These are supernovae (explosions of stars) that specifically occur when a white dwarf star becomes too massive by gaining mass from a binary partner star. What’s important for these studies though is that Type Ia supernovae serve as reliable distance indicators, or “standard candles”. They can be considered standardized candles because they have relatively uniform intrinsic brightness. With some additional calibrations based on the ‘light curves’ (how long the light from the explosion lasts), it is possible to tell exactly how bright the explosion was. When astronomers know how bright something truly was, they can infer how far away that object is. This is like knowing how bright a 60-Watt lightbulb is, and being able to tell based on how bright you see it whether it is a foot away or 10 feet away. Few objects in astronomy are standardizable in this way, and combined with the immense brightness of these explosions, Type Ia supernovae are indispensable tools in modern studies of the Universe’s expansion.
How do astronomers know when they see a Type Ia supernova? Any star explosion (supernova or nova) results in a very bright object that wasn’t there before (or wasn’t nearly as bright). But, astronomers also typically need to use ‘spectroscopy’ of the supernova light to tell if it is a Type Ia supernova. Spectroscopy involves dispersing light from an astronomical object into its component wavelengths (spectrum) and analyzing the resulting spectrum. However, spectroscopy needs specific equipment and takes hours to measure a single object. Many surveys, including DES, do not take spectroscopic measurements. DES measures all objects in 4 or 5 ‘photometric bands’, which act as color filters, but give much less precise information about the spectrum. The benefit of this approach is that it is much faster, allowing DES to study many more objects than a spectroscopic survey. DES Type Ia supernovae studies have thus previously relied on other telescopes assisting them by taking spectroscopic measurements after DES detected a possible Type Ia supernova.
That brings us to this DES paper. While most previous cosmological samples of Type Ia supernovae are classified based on their spectra, including previous DES results, this paper classifies the DES supernovae using a machine learning algorithm applied to their light curves in the four photometric bands that DES measures itself (the g,r, i and z bands). These light curves are shown in Figure 2. The machine learning algorithm compares the photometric measurements to known Type Ia supernovae light curves, in order to filter out other types of supernovae. After these checks, the authors found 1635 Type Ia supernovae in the redshift range that pass the quality selection criteria, meaning they can be used to constrain cosmological parameters. This is the largest, most homogeneous sample of high-redshift Type Ia supernovae ever discovered. The only previous analyses that had similar numbers of objects needed to use discoveries from many different surveys, which can induce errors. Spectroscopic redshifts were acquired from a dedicated follow-up survey of the host galaxies (typically well after the supernova had faded away). The data was also combined with 194 low-redshift Type Ia supernovae from other surveys, since DES does not cover very low redshifts (less than 0.1).
This paper mainly describes the final results, but the full details of the analysis and the many checks and calibrations needed for it were spread across more than a dozen papers, as discussed in the paper.
Figure 2. All DES light curves, showing observed magnitudes in g, r, i, and z bands (left to right respectively) normalized by the maximum brightness of each light curve, and with the time-axis de-redshifted to the rest-frame. Each light curve has been arbitrarily offset by their redshift, with higher-redshift objects higher on the plot (as labeled on vertical axis). As seen, supernovae brighten quickly and then fade over a timescale of weeks.
The study of these 1635 Type Ia supernovae’s brightness measurements and redshifts allow for detailed analysis of the Universe’s expansion. This paper concludes with 99.99998% confidence that the Universe is accelerating in its expansion. Such a robust result was only possible because of the wide redshift range of the data sample. The data for distances (obtained from the brightness) and redshifts was compared with theoretical predictions for several different cosmological models (as seen in Figure 3). In many cases, the supernovae data was also combined with other datasets, including the Year-3 DES galaxy clustering and weak lensing results (discussed in these two darchives), baryon acoustic oscillations measurements from the eBOSS survey, and cosmic microwave background measurements from the Planck satellite. The various data combinations are consistent with the ‘standard’ model of a flat Universe and with dark energy acting as a ‘cosmological constant’. However, the data also shows a better fit with time-varying dark energy models, where the dark energy equation of state grows (closer to zero) with time (see Figure 4). One implication of these time-varying dark energy models would be that the Universe is younger than the age calculated in a Universe where dark energy is a cosmological constant. The best fit model found in this analysis suggests the Universe may only be about 13.4 billion years old, rather than 13.8 billion years.
Figure 3: Hubble diagram showing the distances and redshifts of the combined dataset of Type Ia supernovae. The 1635 new DES supernovae are in blue, and are shaded by their probability of being a Type Ia (key on right side). Most outliers are likely contaminants (pale blue). The inset shows the number of supernovae as a function of redshift (same redshift range as the main plot). The lower panel shows the difference between the data (points with error bars) and a few different cosmological models.
Figure 4: Contour constraints from various combinations of data on the dark energy equation of state (w0) and how it changes with time (wa). The contours outline what numbers for each parameter match the data well. The standard ‘cosmological constant’ model (dotted line) predicts w0=-1, and wa=0, meaning dark energy does not change with time. As seen, most combinations of datasets (the various contours, with the dark orange being all of the datasets together) are a better fit with w0 larger than -1, and a wa smaller than zero, meaning dark energy would change with time.
The possibility that dark energy is time-varying and not a ‘cosmological constant’ is tantalizing. Perhaps equally significant about this paper though is the new technique it pioneers. The extensive work and tests shown in this and related DES papers, including Vincenzi et al. 2024, prove that photometric identifications of Type Ia supernovae can also be used for precise cosmological measurements. The authors show that neither contamination due to supernova classification (i.e., occasionally mistaking a ‘core-collapse’ supernova to be a Type Ia supernova due to lack of spectra) or host-galaxy matching for the redshift measurements, are limiting factors for cosmological analyses. Thus, Type Ia supernovae studies are not limited to requiring live spectroscopic follow-up. In fact, the papers show that there are other factors that will be more significant to the success of future Type Ia supernovae studies, for example, obtaining a higher-quality low-redshift sample, adding Ultraviolet and Near-Infrared information to light-curve fitting models, controlling selection effects across the entire redshift range, improving understanding of supernova intrinsic scatter properties and the role played by interstellar dust. Thus, this paper represents a breakthrough in techniques. Future photometric surveys, like that of the Rubin Observatory being built in Chile, will undoubtedly reference this paper as they prepare their own Type Ia supernovae studies. Meanwhile, the final Dark Energy Survey analyses, which will include these Type Ia supernovae results, along with DES studies of baryon acoustic oscillations, galaxy clustering, and weak lensing, are still in progress.
Thanks to paper co-author Tamara Davis for some helpful edits on this article. For more discussion of this work, including comments from some of the lead researchers, please see this press release from January 2024.
DArchive Author: Caio Cavarsan
Caio grew up in Brazil and is currently an undergraduate physics major at William Jewell College in Liberty, Missouri, USA. Caio is interested in astrophysics and worked on a clustering redshifts project for DES during the summer of 2024. Outside of doing physics, Caio is a member of the William Jewell swim team and enjoys playing basketball and drinking coffee.
DArchive Author: Ross Cawthon
Ross is a professor at William Jewell College in Liberty, Missouri, USA. He works on various projects studying the large-scale structure of the Universe using the millions of galaxies DES observes. These projects include galaxy clustering, correlations of structure with the cosmic microwave background and using the structure of the Universe to infer the redshifts of galaxies. Ross also coordinates Education and Public Outreach efforts in DES, including managing the darchives and social media.