The paper "Generalized Scaling Laws in Turbulent Flow at High Reynolds Numbers" introduces a novel method for analyzing turbulent flow time series data. It focuses on the "Van Atta effect," which describes the persistence of velocity difference correlations across different spatial scales. The authors demonstrate that these correlations exhibit a power-law scaling behavior, revealing a hierarchical structure within the turbulence. This scaling law can be used as a robust feature for characterizing and classifying different turbulent flows, even across varying Reynolds numbers. Essentially, by analyzing the power-law exponent of these correlations, one can gain insights into the underlying dynamics of the turbulent system.
One year after the groundbreaking image of M87's black hole shadow, the Event Horizon Telescope (EHT) collaboration released further analysis revealing the dynamics of the surrounding accretion flow. By studying polarized light emissions, the team discerned the structure of the magnetic fields near the event horizon, critical for understanding how black holes launch powerful jets. The observations show a turbulent, swirling accretion flow, dominated by tangled magnetic field lines, which are thought to be crucial in powering the jet and extracting energy from the black hole's rotation. This reinforces the understanding of M87 as an active black hole, actively accreting material and launching energetic jets into intergalactic space. The polarized view provides a crucial piece to the puzzle of black hole physics, helping confirm theoretical models and opening new avenues for future research.
HN commenters discuss the implications of the new M87 image, focusing on the dynamic nature of the accretion disk and the challenges of imaging such a distant and complex object. Some express awe at the scientific achievement, while others delve into the technical details of Very Long Baseline Interferometry (VLBI) and the image reconstruction process. A few question the interpretation of the data, highlighting the inherent difficulties in observing black holes and the potential for misinterpretation. The dynamic nature of the image over time sparks discussion about the complexities of the accretion flow and the possibilities for future research, including creating "movies" of black hole activity. There's also interest in comparing these results with Sagittarius A, the black hole at the center of our galaxy, and how these advancements could lead to a better understanding of general relativity. Several users point out the open-access nature of the data and the importance of public funding for scientific discovery.
Summary of Comments ( 2 )
https://news.ycombinator.com/item?id=43292927
HN users discuss the Van Atta method described in the linked paper, focusing on its practicality and novelty. Some express skepticism about its broad applicability, suggesting it's likely already known and used within specific fields like signal processing, while others find the technique insightful and potentially useful for tasks like anomaly detection. The discussion also touches on the paper's clarity and the potential for misinterpretation of the method, highlighting the need for careful consideration of its limitations and assumptions. One commenter points out that similar autocorrelation-based methods exist in financial time series analysis. Several commenters are intrigued by the concept and plan to explore its application in their own work.
The Hacker News post titled "Extracting time series features: a powerful method from a obscure paper [pdf]" linking to a 1972 paper on the Van Atta method sparked a modest discussion with several insightful comments.
One commenter points out the historical context of the paper, highlighting that it predates the Fast Fourier Transform (FFT) algorithm becoming widely accessible. They suggest that the Van Atta method, which operates in the time domain, likely gained traction due to computational limitations at the time, as frequency-domain methods using FFT would have been more computationally intensive. This comment provides valuable perspective on why this particular method might have been significant historically.
Another commenter questions the claim of "obscurity" made in the title, arguing that the technique is well-known within the turbulence and fluid dynamics communities. They further elaborate that while the paper might not be widely recognized in other domains like machine learning, it is a fundamental concept within its specific field. This challenges the premise of the post and offers a nuanced view of the paper's reach.
A third commenter expresses appreciation for the shared resource and notes that they've been searching for methods to extract features from noisy time series data. This highlights the practical relevance of the paper and its potential application in contemporary data analysis problems.
A following comment builds on the discussion of computational cost, agreeing with the initial assessment and providing additional context on the historical limitations of computing power. They underscore the cleverness of the Van Atta method in circumventing the computational challenges posed by frequency-domain analyses at the time.
Finally, another commenter mentions a contemporary approach using wavelet transforms, suggesting it as a potentially more powerful alternative to the Van Atta method for extracting time series features. This introduces a modern perspective on the problem and offers a potentially more sophisticated tool for similar analyses.
In summary, the discussion revolves around the historical significance of the Van Atta method within the context of limited computing resources, its perceived obscurity outside its core field, its practical relevance to contemporary data analysis, and potential alternative modern approaches. While not a lengthy discussion, the comments provide valuable context and insights into the paper and its applications.