10 new gravitational waves detected in LIGO-Virgo O3a data

(ORDO NEWS) — Over the past seven years, scientists from the LIGO-Virgo (LVC) collaboration have detected 90 gravitational wave signals. Gravitational waves are disturbances in the fabric of space-time that propagate outwards from cataclysmic events such as the merger of binary black holes (BBH).

During observations in the first half of the last experimental cycle, which lasted for six months in 2019, the collaboration recorded signals from 44 BBH events.

However, new outliers were hidden in the data. Expanding the search, an international team of astrophysicists reexamined the data and found 10 additional black hole mergers, all outside the detection threshold of the original LVC analysis.

The new mergers hint at exotic astrophysical scenarios that, so far, can only be studied with the help of gravitational wave astronomy.

“With gravitational waves, we are beginning to observe a wide range of black holes that have merged over the past few billion years,” says physicist Seth Olsen, a Princeton University graduate student who led the new analysis.

Each observation, he says, contributes to our understanding of how and Black holes are evolving, and the key to recognizing them is finding efficient ways to separate signals from noise.”

Olsen will talk about how his group discovered the mergers on April 11 during a session at the April APS 2022 meeting. He will also answer questions from members of the media during an online press conference on April 10 at 10 a.m. EDT.

Remarkably, the observations included phenomena from both high and low mass black holes, filling predicted gaps in the black hole mass spectrum where few sources have been found. Most models of nuclear physics suggest that stars cannot collapse into black holes with masses around 50 to 150 times that of the sun.

“When we find a black hole in this mass range, it tells us that there is something else about how the system formed,” says Olsen, “because there is a strong possibility that a black hole in the upper mass range is the product of a previous merger.”

Nuclear physics models also suggest that stars less than twice the mass of the Sun become neutron stars rather than black holes, but nearly all observed black holes are more than five times the mass of the Sun.

Observations of low-mass mergers could help bridge the gap between neutron stars and the lightest known black holes.

A small number of black holes have already been discovered for the upper and lower mass ranges, Olsen said, but the new results show that these types of systems are more common than we thought.

The new results also include a system scientists have never seen before: A heavy black hole orbiting in one direction is swallowing up a much smaller black hole that is orbiting it in the opposite direction.

“The rotation of the heavier black hole is not exactly counter-clockwise to the orbit,” says Olsen, “but rather tilted somewhere between sideways and upside down, which tells us that this system may come from an interesting subpopulation of BBH mergers, where the angles between BBH orbits and the rotations of black holes are random.”

Identifying events such as black hole mergers requires a strategy to distinguish significant signals from background noise in the observational data. It’s not like smartphone apps that can analyze music – even if it’s playing in a noisy public place – and determine what song is playing.

Just as such an application compares music against a database of patterns or frequency signals of famous songs, a gravitational wave search program compares observational data against a catalog of known events, such as black hole mergers.

To find the 10 extra events, Olsen and colleagues analyzed the LVC data using the “IAS pipeline,” a technique pioneered at the Institute for Advanced Study and led by Princeton astrophysicist Matthias Zaldarriaga. The IAS pipeline differs in two important ways from the pipelines used by LVC.

First, it uses advanced data analysis and numerical techniques to improve the signal processing and computational efficiency used by LVC.

Second, it uses a statistical methodology that sacrifices some sensitivity to sources most likely to miss LVC approaches in order to gain sensitivity to sources most likely to miss LVC approaches, such as rapidly rotating black holes.

Previously, Zaldarriaga and his team used the IAS program to analyze data from earlier LVC runs and similarly identified black hole mergers that had been missed in the first run analysis.

According to Olsen, it is impossible to simulate the entire universe using computational tools, or even the staggeringly wide range of ways in which black holes form. But tools like the IAS pipeline, he says, “could lay the groundwork for even more accurate models in the future.”

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