Reviewed by Lexie CornerApr 5 2024
When a child runs about the home and playground, it might appear as though they have unlimited energy. While going about their daily lives, most adults, on the other hand, move more slowly.
Like people, stars eventually lose their luster. They spin quickly at birth, but over billions of years, they slow down. The Sun, which is 4.5 billion years old, rotates roughly once a month.
After around a billion years, stars of the same mass and age will rotate at the same speed. If the mass and rotation rate of a star are known, its age can be ascertained. NASA plans to deploy the Nancy Grace Roman Space Telescope by May 2027, enabling it to measure the spin speeds and age of hundreds of thousands of stars.
While age guessing is a common game at carnivals, astronomers find it extremely difficult to estimate the ages of stars. A star like the Sun remains largely unaltered for billions of years after reaching its mature phase, or steady nuclear fusion. The star’s rotation period, or the speed at which it rotates, is one exception to this rule.
NASA’s Nancy Grace Roman Space Telescope, scheduled for launch in May 2027, is expected to provide fresh insights into the stellar populations inside the Milky Way Galaxy by measuring hundreds of thousands of stars’ rotation periods.
Stars spin quickly as they are born. However, over billions of years, stars with masses comparable to the Sun’s will progressively slow down. The star's magnetic field and the stellar wind, a stream of charged particles, combine to produce that slowing. The star spins more slowly as a result of the interactions, in the same way that an ice skater slows down as they stretch their arms.
The intensity of the star’s magnetic field affects this phenomenon, known as magnetic braking. Stronger magnetic fields force faster-spinning stars to slow down more quickly. Due to the impact of these magnetic fields, stars of the same mass and age will spin at the same rate after around a billion years. Thus, the age of the star can be calculated, provided its mass and rotation rate are known. It is possible to study how the galaxy began and changed throughout time by determining the ages of a large population of stars.
Astronomers calculate the rotation rate of a distant star by searching for variations in the brightness of the star caused by starspots. Similar to sunspots on our Sun, starspots are darker, cooler regions on a star’s surface. The star will appear somewhat fainter when a starspot is visible than when it is located on the star’s far side.
If a star contains a single, huge spot, it will fade and brighten in a regular pattern as the spot rotates in and out of view. This dimming is distinct from a comparable effect induced by a transiting exoplanet. However, a star might have hundreds of spots distributed over its surface at any given time, and those spots change over time, making it considerably more difficult to discern periodic signals of dimming from the star’s rotation.
A team of astronomers at the University of Florida is developing new ways to derive a rotation period from observations of a star’s brightness over time as part of a NASA-funded project called the Nancy Grace Roman Space Telescope.
They use a convolutional neural network, an artificial intelligence, to assess light curves, which are plots of a star's brightness over time. To do this, the neural network must first be trained using simulated light curves. Zachary Claytor, a postdoctoral associate at the University of Florida and the project's science principal investigator, developed a program called “butterpy” to produce such light curves.
This program lets the user set a number of variables, like the star’s rotation rate, the number of spots, and spot lifetimes. Then it will calculate how spots emerge, evolve, and decay as the star rotates and convert that spot evolution to a light curve – what we would measure from a distance.
Zachary Claytor, Science Principal Investigator and Postdoctoral Associate, University of Florida
The team has already applied its trained neural network to data from NASA's TESS (Transiting Exoplanet Survey Satellite). Longer stellar rotation periods are more difficult to estimate properly due to systematic influences, but the team's trained neural network was able to do so with the TESS data.
The upcoming Roman Space Telescope will undertake three primary community surveys, including the Galactic Bulge Time Domain Survey, which will collect data from hundreds of millions of stars. Roman will peer toward the galaxy's core, which is densely populated with stars, to see how many of them change brightness over time. These observations will enable a variety of scientific inquiries, including the hunt for distant exoplanets and the determination of star rotation rates.
The astronomical community is still developing the specific survey design. The information provided by the NASA-funded study on star rotation might benefit potential survey tactics.
We can test which things matter and what we can pull out of the Roman data depending on different survey strategies. So when we actually get the data, we’ll already have a plan. We have a lot of the tools already, and we think they can be adapted to Roman.
Jamie Tayar, Principal Investigator and Assistant Professor, University of Florida