The majority of the exoplanets, more than 5,500 in total, have been identified by the Kepler satellite telescope.
However, NASA’s Transiting Exoplanet Survey Satellite (TESS) has been adding additional alien worlds to the universe’s ever-expanding planetary list.
A group of scientists led by University of New South Wales Ph.D. student Priyashkumar Mistry found eight new exoplanets using statistical methods to sift through enormous amounts of TESS data.
According to NASA, each of these newly discovered planets is a “super-Earth,” a type of exoplanet that is larger than Earth but smaller than Neptune.
TESS has confirmed around 400 exoplanets to date, with another 6,977 planets awaiting confirmation. The satellite observes the surrounding stars and records any dips or fluctuations in their brightness.
Such dips imply that something crossed between Earth and the star, which could be a new exoplanet.
“If this orbital motion ever comes between us and the star we will observe a dip in the brightness of that observed star. This is what we call a transit,” said Mistry, while speaking to Space.com.
Mistry and his team used the Validation of Transiting Exoplanets Using Statistical Tools (VaTEST) project to find anomalies that assisted in detecting the presence of exoplanets in TESS data.
The significance of statistical techniques
Such dips are not caused just by transiting exoplanets; they can also be caused by a star orbiting another star (binary system), which may have generated a transit-like signal.
According to Mistry, the transit method is only useful for calculating the radius of an orbiting body. In general, radial velocity (RV) is employed to calculate the mass of a transiting object.
As astronomers continue to observe only one star, it can take a long time to discover the RV signal, especially if the exoplanet has a lengthy orbital period.
VaTEST, on the other hand, provided Mistry and his team with another method to confirm whether these transiting events were caused by circling exoplanets.
“The tool takes in the transit data and some inputs such as transit depth, period, TESS identifier, etc. Then, based on that it starts fitting different models on the data and performs some probability calculations. And then finally it calculates False Positive Probability (FPP) if it turns out to be < 1%, then we can validate that transit signal as a planetary transit,” Mistry stated.
