Artificial Intelligence Unlocks Precise Map of Dark Energy in the Universe
Scientists have long been grappling with the enigma of dark energy, the mysterious force responsible for the universe’s accelerated expansion. But now, a team of researchers led by Niall Jeffrey from University College London has made a groundbreaking discovery using artificial intelligence (AI) techniques. Their findings suggest that computers may hold the key to unraveling the secrets of dark energy with unprecedented precision.
The team collaborated with the Dark Energy Survey to create a supercomputer simulation of the universe by analyzing measurements of visible matter and dark matter. While dark energy propels the universe outward, dark matter remains invisible due to its lack of interaction with light. By employing AI, the researchers were able to extract a detailed map of the universe spanning the last seven billion years, highlighting the actions of dark energy. This remarkable dataset encompasses approximately 100 million galaxies across 25% of the Southern Hemisphere sky.
Without AI, generating such a map based on the first three years of observations from the Dark Energy Survey would have required significantly more data. In fact, Niall Jeffrey explains that “you’d need four times as much data using the standard method.” The AI approach not only doubled the precision in measuring dark energy but also saved an enormous amount of time and effort. Jeffrey adds, “If you wanted to get this level of precision and understanding of dark energy without AI, you’d have to collect the same data three more times in different patches of the sky. This would be equivalent to mapping another 300 million galaxies.”
Dark energy presents a perplexing challenge for scientists. It is an unknown force that drives galaxies away from each other at an accelerating rate, accounting for approximately 70% of the universe’s energy and matter budget. When combined with dark matter, which makes up 25% of this budget, we are left with only 5% of the visible universe. As Jeffrey explains, “We really don’t understand what dark energy is; it’s one of those weird things. It’s just a word that we use to describe a kind of extra force in the universe that’s pushing everything away from each other as the universe’s expansion continues to accelerate.”
The team’s research supports the cosmological constant, represented by the Greek letter lambda, as a viable explanation for dark energy. The cosmological constant was first introduced by Albert Einstein in his theory of general relativity to account for a static universe. However, subsequent observations by Edwin Hubble revealed that the universe is expanding, leading Einstein to dismiss the cosmological constant as his “greatest blunder.” Yet, in 1998, astronomers discovered that the universe was not only expanding but also accelerating. Dark energy emerged as a possible explanation for this phenomenon, and the cosmological constant was resurrected.
Although the team’s findings align with the cosmological constant, the vast disparity between theory and observation remains a significant challenge. The lambda value predicted by quantum physics is 120 orders of magnitude smaller than what observations suggest. This discrepancy has earned the cosmological constant the title of “the worst theoretical prediction in the history of physics.” While the research sheds light on the expansion of the universe and gravity’s role in it, it does not bridge the gap between theory and observation.
Despite this limitation, the study demonstrates the potential of AI in analyzing simulated models of the universe and identifying crucial patterns that may elude human perception. Niall Jeffrey emphasizes that this requires a specific form of AI trained to recognize patterns in the cosmos. It cannot be as simple as inputting simulations into AI systems like ChatGPT, which may fabricate information when faced with unknowns. Further advancements are needed to ensure reliable results in combining science and AI.
The Dark Energy Survey is set to continue for another six years, providing additional data that, combined with observations from the Euclid telescope, will enhance our understanding of the universe’s large-scale structures. These advancements will refine cosmological models and enable even more precise simulations, potentially unraveling the mysteries of dark energy.
The team’s research, available as a preprint on the arXiv paper repository, marks a significant step forward in our quest to comprehend the forces shaping the universe. While the puzzle of dark energy remains, these findings offer hope that AI and advanced simulations will bring us closer to unlocking its secrets.
