A research team has used the DiRAC facility teamed with artificial intelligence (AI) techniques to infer the influence and properties of dark energy more precisely from a map of dark and visible matter in the Universe covering the last seven billion years. The study was carried out by the Dark Energy Survey collaboration (DES), which during 2013-19 catalogued hundreds of millions of galaxies, using photographs taken by one of the world’s most powerful digital cameras, mounted on a telescope at the National Science Foundation’s Cerro Tololo Inter-American Observatory in Chile. The new analysis has doubled the precision at which key characteristics of the Universe, including the overall density of dark energy, could be inferred from the map. Dark energy is the mysterious force that is accelerating the Universe’s expansion and is thought to make up about 70% of the content of the Universe (with dark matter, invisible stuff whose gravity pulls galaxies, making up 25%, and normal matter just 5%). The increased precision has ruled out models of the Universe that might previously have been conceivable.
In line with a previous analysis first published in 2021, the findings suggest that matter in the Universe is more smoothly spread out – less lumpy – than Einstein’s theory of general relativity would predict. Currently, this smoothness is at odds with what would be predicted based on analysis of the cosmic microwave background (CMB) – the light left over from the Big Bang.
Lead author Dr Niall Jeffrey (UCL Physics & Astronomy) said: Using AI to learn from computer-simulated universes, we increased the precision of our estimates of key properties of the Universe by a factor of two. To achieve this improvement without these novel techniques, we would need four times the amount of data, equivalent to mapping another 300 million galaxies.
Co-author Dr Lorne Whiteway (UCL Physics & Astronomy) said: Our findings are in line with the current best prediction of dark energy as a ‘cosmological constant’ whose value does not vary in space or time. However, they also allow flexibility for a different explanation to be correct. For instance, it still could be that our theory of gravity is wrong.
The DES map was obtained through a method called weak gravitational lensing – that is, seeing how light from distant galaxies has been bent by the gravity of intervening matter on its way to Earth. The collaboration analysed distortions in the shapes of 100 million galaxies to infer the distribution of all matter, both dark and visible, in the foreground of those galaxies. The resulting “matter map” covers a quarter of the sky in the Southern Hemisphere.
For the new study, the team used the DiRAC computers Wilkes (Cambridge) and Tursa (Edinburgh) to run the “Gower St.” simulations of different universes based on the DES matter map, each underpinned by a different mathematical model of the universe. A machine learning model was then used to extract the information relevant for cosmological models. A second machine learning tool, learning from the many examples of simulated universes with different cosmological models, looked at the real observed data and gave the odds on any cosmological model being the true model of our Universe. This new technique allowed researchers to extract much more information from the maps than would be possible with previous methods.
The next phase of dark universe projects – including the European Space Agency mission Euclid, launched last summer – will greatly increase the quantity of data we have on the large-scale structures of the Universe, helping researchers determine if the unexpected smoothness of the Universe is a sign current cosmological models are wrong or if there is another explanation.
Further details about the Dark Energy Survey can be found at https://www.darkenergysurvey.org/
