Blackholes are one of nature’s most intriguing objects, a singularity surrounded by a strong gravitational pull that light itself cannot escape. In 2017, the first-ever image of a black hole was published. Since then, many have been observed and studied. Scientists usually observe a blackhole indirectly from the accretion disk surrounding it. Accretion disks are formed when matter, like gas and stars, swirl around the black hole and eventually fall into it.
Observing the accretion disks of black holes, scientists have noticed that they sometimes shine brightly and, at other times, dim and even flicker in mysterious patterns. Scientists have been trying to understand these flickers and their origin. In a new study, researchers from the Indian Institute of Science (IISc) observed the black hole X-ray binary IGR J17091–3624 (henceforth just IGR) and studied the variations in its flickers.
An X-ray binary black hole is a system in which two stars orbit each other, and one of the stars has transformed into a black hole. When the binary system produces intense X-rays, it is named an X-ray binary black hole. The team was investigating whether the IGR system’s observed variability or flicker is predominantly random and without an underlying pattern (stochastic) or just appears random but is determined by an underlying predictable (deterministic) but chaotic behaviour.
The research is motivated by the similarity between IGR and the better-known black hole system GRS 1915+105, which, in some instances, exhibits clear signatures of chaotic dynamics. GRS 1915+105 (just GRS henceforth) has long been studied due to the highly diverse timing of its flickers, called temporal patterns. Previous works classify some of its temporal patterns as chaotic and others as stochastic.
IGR has exhibited similar temporal patterns, leading researchers to regard it as a twin of GRS. However, earlier studies have found that its patterns are predominantly stochastic rather than chaotic. This mismatch is because IGR is much fainter, and its data are more severely contaminated by instrumental and Poisson noise, the natural statistical fluctuations in the number of photons detected.
So, the team tried new methods to remove the static from the measurements. They applied multiple noise-reduction (denoising) techniques, such as Nonlocal Means and an Adaptive Denoising Algorithm, to the light curves to filter out as much noise as possible while keeping the underlying signal.
They then used established and novel approaches—such as correlation integrals (CI), principal component analysis (PCA), and singular value decomposition (SVD)—to check for signs of determinism in the denoised light curves. They discovered that some flickers weren’t random but revealed underlying patterns, suggesting that IGR resembles GRS.
Out of nine temporal classes (or patterns) of IGR, four classes remain stochastic after denoising. Once the noise was removed, three classes showed signs of non-stochastic (likely chaotic) behaviour. Two classes show mixed or inconclusive results, though the authors suspect that one of these two classes behaves deterministically. These changes to the flicker patterns or temporal classes are believed to be caused by different feeding modes of the black hole, called accretion flow types.
Throughout their study, the researchers also compared these temporal classes with earlier spectral analyses or the type of light each pattern emits. Their findings suggest that IGR, like GRS, can transition among different accretion flows or feeding modes, such as when the disk around the black hole is slim, thick, or has a significant hot gas region. By studying how the flickers and the disk structure match up, the scientists hope to learn what drives each mode change and how matter spirals into a black hole under extreme gravity.
Their results confirm that IGR likely hosts the same variety of dynamical regimes as GRS rather than being purely stochastic. The study also shows that advanced denoising is crucial for revealing hidden dynamical structures in faint X-ray sources. Moreover, it provides valuable information about how matter falls into a black hole and how these events are seen from the Earth.
This research news was partly generated using artificial intelligence and edited by an editor at Research Matters