Overturning decades of popular conjecture that theoretical string theory cannot be tested, scientists have robustly proved that it can be for the first time.
Through the development of artificial intelligence tools that combine statistics and machine learning, physicists at King’s College London and their collaborators have used a popular dark matter candidate to test the extra dimensions of space that sit at the core of string theory.
First gaining prominence in the late 1960s as a potential ‘theory of everything’, string theory is the idea that reality is made up of an incredible number of vibrating strings, all smaller than fundamental particles like electrons.
As these strings vibrate, they are thought to produce effects in up to ten dimensions which helps explain everything, from the effects of quantum mechanics to gravity and general relativity – uniting the two theories that underlie most of modern physics.
However, no experiment has been able to robustly test or prove string theory correct, in part due to the inability of a single machine to measure the four fundamental forces that make up the world – until now.
To establish a test case to compare string theory against, the team asked the hypothetical question: If the axion, a leading dark matter candidate thought to have a frequency like a wave, was discovered tomorrow, what would happen to string theory?
Published in Physical Review D, the study modelled two different possible masses of the axion detectable by two different experiments, DM Radio and ADMX, using a technique called Bayesian inference to see what this would imply for string theory. Comparing the two models, they found if the heavier axion turned out to be detected, then this does very specific things to the extra dimensional space predicted in string theory.
Surveying millions of different models or topologies of string theory, the team discovered that only a subset of theories would fit the heavier axion – meaning that given a future detection of the axion one could verify that some models did not work through rigorous testing; the first time this has been done.
Author Dr David Marsh, Ernest Rutherford Fellow at King’s College London, said “The popular understanding that you can’t experimentally test string theory is no longer true. While previous studies have tested a few different models of string theory, our computational approach improves on this with rigorous testing across millions of different models.
“Rigorous falsification is the core of scientific experiment, and by finally applying this to string theory we finally have the tools to test this vaunted ‘theory of everything’. Finally touching experimentation here is an exciting step forward for theoretical particle physics.”
Building on their rigorous statistical methods, the team also developed a parallel avenue to test string theory using real-world data. Cosmic Microwave Background (CMB) is the cooled radiation that fills the universe, thought to be left over from the Big Bang. However, there exists a small discrepancy between its temperature distribution and the distribution of Hydrogen gas in the universe as measured by the Lyman-alpha forest, an important astrophysical probe. King’s Dr Keir Rogers showed previously this could be solved if dark matter was ultralight and described by a wave function, another prediction of axion models of dark matter.
In a full analysis of the cosmological data from the CMB and the Lyman alpha forest, the group developed a predictive model for the extra dimensions of string theory. They identified just a tiny handful of string theory models capable of explaining the discrepancy in the data.
Using their predictive model, the group then showed that this handful of string theory models also predicted new types of particles that could help make up complex dark matter. If these new predicted particles were found, string theory would once again be falsifiable in a major step forward for theoretical physics.
This research was led by King's College London postdoc Dr Mudit Jain, funded by a Leverhulme Research Project.