It is thought that as stars grow older, their spin slows down, but new research suggests ageing stars rotate faster than previously thought.
Just like planets, stars like the Sun rotate around an axis.
As they age, their spin slows down due to the star’s magnetic field acting on its stellar wind – a flow of gas moving away from the star – like a brake.
In the past, researchers have used this process, known as magnetic braking, to calculate the age of stars.
The method involves pinpointing dark spots on the surface of stars and track them as they move with the star’s spin.
However, University of Birmingham scientists point out that while the method works when measuring spin in younger stars, older stars have fewer star spots, which can make the effects of “weakened” magnetic braking hard to confirm.
The team used a technique known as asteroseismology, which involved measuring the oscillations caused by sound waves trapped inside the star.
Dr Oliver Hall, from the University of Birmingham and lead author on the paper published in Nature Astronomy, said: “Although we’ve suspected for some time that older stars rotate faster than magnetic braking theories predict, these new asteroseismic data are the most convincing yet to demonstrate that this ‘weakened magnetic braking’ is actually the case.
“Models based on young stars suggest that the change in a star’s spin is consistent throughout their lifetime, which is different to what we see in these new data.”
The researchers said understanding how the magnetic field interacts with rotation could help shed light on the Sun’s activity over the next several billion years,
Study co-author Dr Guy Davies, also from the University of Birmingham, said: “These new findings demonstrate that we still have a lot to learn about the future of our own Sun as well as other stars.
“This work helps place in perspective whether or not we can expect reduced solar activity and harmful space weather in the future.
“To answer these questions we need better models of solar rotation and this work takes an important step towards improving the models and supplying the data needed to test them.”