Research News

Without more data, a black hole's origins can be 'spun' in any direction

Researchers say that current results depend on models rather than data

Clues to a black hole's origins can be found in the way it spins, which is especially true for binaries — when two black holes circle close together before merging. The spin and tilt of the respective black holes just before they merge can reveal whether they arose from a quiet galactic disk or a more dynamic cluster of stars.

Astronomers have been hoping to tease out which of these origin stories is more likely by analyzing the 69 confirmed binaries detected to date.

However, a new study finds that, for now, the current catalog of binaries is not enough to reveal anything fundamental about how black holes form.

In the study, published in Astronomy and Astrophysics Letters, physicists at the Massachusetts Institute of Technology tested whether the same data would yield the same conclusions when worked into slightly different theoretical models of how black holes form. A black hole's origins can therefore be "spun" in different ways, depending on a model's assumptions of how the universe works. The research was supported in part by a grant from the U.S. National Science Foundation and by the NSF-funded Laser Interferometer Gravitational-Wave Observatory Laboratory.

"Our paper shows that your result depends entirely on how you model your astrophysics, rather than the data itself," says study co-author Sylvia Biscoveanu, an NSF Graduate Research Fellow working in the LIGO Laboratory. "When you change the model and make it more flexible or make different assumptions, you get a different answer about how black holes formed in the universe."

First author Salvatore Vitale says, "We need more data than we thought if we want to make a claim that is independent of the astrophysical assumptions we make."

Just how much more data will astronomers need?  

"The measurements of the spins we have now are very uncertain. But as we build up a lot of them, we can gain better information. Then we can say, no matter the detail of my model, the data always tells me the same story — a story that we could then believe."