The solar cycle is an approximate 11-year period during which the Sun’s magnetic activity rises and falls. The cycle begins relatively calmly, however, as it progresses, the magnetic field becomes more aggressive leading to a surge in sunspot numbers, solar flares, and coronal mass ejections. These eruptions can disrupt satellites and power grids and thus have a direct impact on our communication technology.
Sound (pressure) vibrations, known as p-modes, are measured on the Sun’s surface. They are important to scientists because they can travel deep into the Sun’s interior and return, bringing back information from inside during the solar cycle.
Solar scientists have been observing these sound waves for decades so that they can map the Sun’s internal structure, much like seismologists use earthquakes to study Earth’s interior. These waves also follow the solar cycle, meaning their pitch shifts with the Sun’s 11-year rhythm of activity.
Led by Dr Rekha Jain, the study published in Solar Physics demonstrates that the AI model can decode p-modes. In the same way a doctor uses sound waves to see inside the human body, p-modes lets scientists “see” the inside of the Sun. By looking at thirty years of p-mode data the researchers have predicted when the pitch of these waves will reach the “calm” phase in the current solar cycle.
“The Sun rules more than daylight; it powers and shapes life on Earth. By using machine learning to listen to the acoustic heartbeat of the Sun, we are trying to track the energy drivers moving from the deep interior toward the surface and beyond, creating a much clearer link between the Sun's interior and the solar disturbances that can disrupt satellites and power grids.” Dr Rekha Jain
Forecasting these waves will help researchers to provide an independent indicator to anticipate periods of high solar activity connected with the magnetic field changes hidden beneath its surface. This makes helioseismology (the study of solar waves) a new tool within the wider field of space weather research. Helioshelioseismology combines solar physics, magnetohydrodynamics, machine learning and atmospheric science to predict and mitigate the effects of solar activity on technology and human society.
By understanding the properties of waves on the sun, the researchers are helping to build the foundation for a future where the Sun’s nature can be better predicted.