{"id":595805,"date":"2026-02-10T06:50:43","date_gmt":"2026-02-10T12:50:43","guid":{"rendered":"https:\/\/cerebrodigital.net\/?p=595805"},"modified":"2026-02-10T07:23:55","modified_gmt":"2026-02-10T13:23:55","slug":"astronomers-using-ai-understand-black-holes","status":"publish","type":"post","link":"https:\/\/cerebrodigital.net\/en\/astronomers-using-ai-understand-black-holes\/","title":{"rendered":"Astronomers continue using AI to understand black holes: results are starting to surprise"},"content":{"rendered":"\n

For decades, supermassive black holes have been among the greatest enigmas of the universe. Today, artificial intelligence has become a key ally in deciphering what telescopes still cannot clearly see. Thanks to neural networks trained with millions of simulations, astronomers are achieving more precise\u2014and in some cases, profoundly revealing\u2014interpretations of these cosmic giants.<\/p>\n\n\n\n

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Artificial intelligence as a new telescope of the cosmos<\/strong><\/strong><\/h2>\n\n\n\n

Direct observation of a black hole remains one of the greatest challenges of modern astronomy. Even ambitious projects such as the Event Horizon Telescope (EHT)<\/strong> do not produce traditional images, but complex reconstructions based on radio signals captured by radiotelescopes distributed across the planet. In this process, a significant portion of the information is discarded or simplified due to its difficult interpretation.<\/p>\n\n\n\n

To address this limitation, researchers at the Morgridge Institute for Research<\/strong> in Wisconsin trained neural networks with millions of simulations of supermassive black holes. These simulations reproduce different physical scenarios and allow AI to learn to identify hidden patterns in incomplete or noisy data. Unlike classical methods, these systems do not ignore ambiguous information but analyze it in depth.<\/p>\n\n\n\n

The result is a shift in approach: AI does not replace telescopes, but amplifies their capacity. By transforming seemingly chaotic data into more detailed models, astronomers can draw conclusions that were previously out of reach, opening a new stage in the study of the extreme universe.<\/p>\n\n\n\n

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Sagittarius A<\/em>: a more extreme black hole than previously thought<\/strong><\/h2>\n\n\n\n

One of the most striking advances from this approach is the new analysis of Sagittarius A, the supermassive black hole at the center of the Milky Way. Until now, its rotation speed was believed <\/a>to be moderate or relatively high. However, AI\u2011based models point to a much more radical scenario.<\/p>\n\n\n\n

Results suggest that Sagittarius A*<\/strong> may be spinning near its maximum speed allowed by physics, with its rotation axis oriented toward Earth. Moreover, the emission detected nearby would not be explained by a relativistic jet, but by extremely hot electrons within the surrounding accretion disk.<\/p>\n\n\n\n

According to the Morgridge Institute for Research, these analyses also indicate that \u201cmagnetic fields in the accretion disk appear to behave differently from the usual theories about these disks.\u201d This detail is crucial, since the rotation speed of a black hole directly influences space\u2011time curvature, the trajectory of light, and system<\/strong> stability, forcing reconsideration of widely accepted models.<\/p>\n\n\n\n

Challenging theories and opening new scientific questions<\/strong><\/h2>\n\n\n\n

Beyond specific results, the true value of using artificial intelligence in astronomy lies in its ability to question established theories<\/strong>. By revealing unexpected <\/a>behaviors in magnetic fields and extreme dynamics, these models suggest that certain fundamental assumptions may require deep revision.<\/p>\n\n\n\n

Researchers themselves emphasize caution. Far from presenting these findings as definitive conclusions, they consider them a first step<\/strong>. As neural networks are trained with more complex simulations and compared with new observations, it will be possible to refine models and validate\u2014or refute\u2014these extreme scenarios.<\/p>\n\n\n\n


<\/strong>La colaboraci\u00f3n entre inteligencia artificial y astronom\u00eda<\/strong> est\u00e1 transformando nuestra comprensi\u00f3n de los agujeros negros. Al rescatar informaci\u00f3n antes desaprovechada y plantear hip\u00f3tesis m\u00e1s exigentes, la IA no solo mejora la resoluci\u00f3n de los datos, sino que empuja a la ciencia a replantear sus propias bases. El universo sigue lleno de misterios, pero ahora contamos con una nueva herramienta para explorarlos.<\/p>\n\n\n\n

Referencia: <\/p>\n\n\n\n