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Universe Today

Just Like Stars, Open Clusters Can Form Binary Pairs Astrophysical Calibration Could "Autotune" Gravitational Wave Detection Something Just Passed Between Us and a Distant Star. When Spacetime Crystallises, a Black Hole is Born The Weirdness of Early Universe SMBHs Gets Even Weirder A Natural Chemistry Laboratory in Protostar Shock Waves Why the Second Full Moon of May is a ‘Blue Minimoon’ NASA TESS Reveals Epic All-Sky Map of Distant Worlds Astronomers Observe the Most Chemically Primitive Galaxy in the Early Universe Where Are All the Intermediate Mass Black Holes? Microlensing Fast Radio Bursts Might Reveal Them When the Sun Tries to Explode and Fails The Sun Just Did Something Nobody Expected and it Kept Going For 19 Days Three Stars, One Extraordinary System and a Drama Still to Come The Definitive Census of Multiple Star Systems Within 10 Parsecs Are Satellite Megaconstellations Accidentally Geoengineering the Earth? The Risk of Stellar Flybys and GJ 710 How Mars Can Help Us Understand 'Marginal' Exoplanets Ultrahigh-energy Cosmic Rays May Be Ultraheavy in Origin NASA's Next-Generation AI Processor Passes Early Testing
A New Model Helps Astronomers Study How Merging Black Holes Ring
Brian Koberl · 2026-05-28 · via Universe Today

Gravitational wave astronomy has been a tremendous breakthrough in our understanding of black holes. We can now detect not just the electromagnetic spectrum of light, but also the very ripples of spacetime created by the mergers of stellar-mass black holes. But our detections of mergers are still tricky and require statistical modeling as well as observational data.

The issue is that even large mergers that release several solar masses of gravitational energy only create faint gravitational waves that hover near the noise level of our current detectors. To separate the data from the noise, we use mathematical models of black hole mergers to identify the events. It's a bit like being able to hear your best friend talking in a noisy, crowded room because you are familiar with the sound of their voice. This modeling approach is powerful but needs to be used carefully. Assume the model too strongly, and you end up only seeing what you expect to see. In a new study, the authors propose a slightly different approach using Bayesian statistics to confirm or rule out events.

When two black holes merge, they first spiral ever closer and ever faster toward each other, creating a gravitational chirp that is fairly easy to detect. After the merger, the resulting black hole experiences a ringdown period where the event horizon of the black hole wobbles like a soap bubble as it settles down. This ringdown period creates fainter gravitational waves, but the details of those waves can tell us more about black holes and general relativity. It's the ringdown period that is the focus of this new work.

Ringdowns are kind of like musical notes. The primary note is the fundamental oscillation, but there are harmonic oscillations as well. But unlike sound waves, where the harmonics are fairly clear, general relativity has non-linear effects that can add depth and complexity. Imagine a student violin and a Stradivarius being played at the same note. The simple harmonics are the same, but there is a complexity that gives the Strad a richer tone.

For this latest study, the authors looked at the ringdown statistics of simulated mergers and found how the statistics can reveal subtle overtones. They then applied this method to publicly available data on black hole mergers and found their method better determined characteristics such as the rotations and masses of the original black holes.

As our observations of gravitational waves get better, with updated detectors and next-generation observatories, the statistics of ringdowns could help us detect smaller and more distant mergers. The statistics may also allow us to probe the details of general relativity.

Reference: Dyer, Richard, and Christopher J. Moore. "Quasinormal Mode Content of Binary Black Hole Ringdowns." *Physical Review Letters* 136.19 (2026): 191403.