But answering such questions becomes tricky if scientists’ inferences about mass and spin are incorrect. So in a recent study by Monash University OzGrav researcher Avi Vajpeyi, he found the question: do these objects have masses and spins? In Avi’s recent research, he built a “depth tracking” tool to determine which masses and spins better describe a particular gravitational wave event.
Avi used this deep tracking tool to study “boxing-day” gravitational waves – GW151226. Inferred from the original work, this gravitational wave was created by the merger of two black holes with standard mass and spin (case A). However, recent work infers that this gravitational wave may come from a strange system: one black hole may be much larger than the other and spin faster (case B). A diagram representing these situations shows them. A graph representing these situations can be seen on the right side of the image below.
The “deep tracking” approach involves delving into these cases to determine which binary black hole system best describes gravitational waves. First, the researchers determined some inferred properties of the merging black hole system, such as the mass ratio q (the ratio of the mass of the smaller black hole divided by the mass of the larger black hole) and xeff (the effective spin of the binary star in the Z direction). On the left side of the image above are the fixed values for the initial and new results. The researchers then used Bayesian inference on these fixed values. They found that both standard (case A) and irregular (case B) black hole pairs can describe GW151226, giving the event a dual character.
This dual nature makes the GW151226 a lot more than initially thought. For example, researchers initially thought GW151226 came from an isolated pair of black holes. However, black hole pairs from Case B are more likely to be found at the center of an active galaxy. So the researchers knew: Did GW151226 come from an isolated pair of black holes? Or are there other “split personality” gravitational wave events? The researchers hope their deep tracking method will address these issues.