Science & Technology (Commonwealth Union) – Research conducted recently from the Dark Energy Survey (DES) which was done jointly includes researchers from the University College London (UCL), mark the initial phase four independent techniques have been combined together to gage the speed of the expanding universe.
When taking into account dark energy, a mysterious force believed to be a factor in the universe’s expansion to speed up, makes up about 70 percent of the total energy of the universe. By evaluating the way the expansion rate has been altered over time, researchers can gain more knowledge in relation to the properties and behaviour of this elusive force.
The analysis draws on the full six-year DES dataset, incorporating observations of weak gravitational lensing, which examines how gravity subtly warps the shapes of distant galaxies, alongside measurements of how galaxies are distributed and clustered.
In the main paper, which synthesises results from 18 companion studies, the team also reports their first measurements based on the combined use of four key probes: baryon acoustic oscillations (BAO), Type Ia supernovae, galaxy clusters, and weak gravitational lensing—an approach envisioned when the DES project was first conceived 25 years ago.
The study produced significantly tighter constraints, sharply reducing the range of viable models describing how the universe evolves. These limits are more than twice as precise as those obtained in earlier DES analyses, while still agreeing with previous DES findings.
To achieve these results, DES researchers made major improvements to weak-lensing techniques, allowing them to more reliably reconstruct the universe’s matter distribution. This involved calculating both how likely pairs of galaxies are to be separated by a given distance and how likely they are to experience similar distortions caused by weak gravitational lensing.
By mapping the distribution of matter across six billion years of cosmic evolution, the combined weak-lensing and galaxy-clustering measurements reveal how the amounts of dark matter and dark energy change over time.
The team then compared their observations with two cosmological frameworks: the widely accepted standard model, known as Lambda cold dark matter (ΛCDM), which assumes a constant dark energy density, and a more flexible alternative, wCDM, in which the equation-of-state parameter w is fixed but the dark energy density can evolve. The results showed strong agreement with the standard cosmological model. While the data were also compatible with the extended model, they did not provide a better fit than ΛCDM.
Professor Ofer Lahav (UCL Physics & Astronomy), former co-chair of the DES Science Committee and Chair of DES:UK, says “It is exciting to see results from the full DES data set, more than two decades after the project was first conceived. The sample of 140 million galaxies with shape measurements is phenomenal. While the headline results support a constant dark energy density, future analyses will test the intriguing possibility of an evolving dark energy.”
The Dark Energy Survey (DES) is a global partnership bringing together more than 400 astrophysicists and researchers from 35 institutions across seven countries. The collaboration is coordinated by the U.S. Department of Energy’s Fermi National Accelerator Laboratory and includes teams from UCL alongside several other universities in the UK.
UCL’s Astrophysics Group, based in the Department of Physics & Astronomy, has played a role in DES since 2004, contributing both to the development of key hardware and to the scientific research.
The survey’s optical corrector, now operating on the U.S. NSF Blanco 4-metre telescope at the NSF Cerro Tololo Inter-American Observatory, was assembled in UCL’s optical laboratory under the leadership of Professors Peter Doel and David Brooks from UCL Physics & Astronomy, with funding from the STFC. Over the past twenty years, UCL researchers, including staff scientists, postdoctoral fellows, and PhD students, have been deeply involved in analysing DES data.
