Unmasking the Universe With AI: How Machine Learning Unravels Black Hole Mysteries

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A new study using machine learning reveals that supermassive black-hole growth in galaxies necessitates cold gas in addition to mergers, challenging previous assumptions and enhancing our understanding of galaxy evolution. Credit: SciTechDaily.comWhen they are active, supermassive black holes play a crucial role in the way galaxies evolve.

She added: “By training a machine to classify mergers, you get a much more truthful reading of what galaxies are actually doing.”, with around 200 billion stars, is only a medium-sized galaxy). These supersized black holes typically weigh between millions and billions of times the mass of our sun. “Determining the role of supermassive black holes in galaxy evolution is crucial in our studies of the universe,” said Ms Avirett-Mackenzie.For decades, theoretical models have suggested black holes grow when galaxies merge.

For the new Bath-led study, the researchers set themselves the challenge of improving the way mergers are classified by studying the connection between black-hole growth and galaxy evolution through the use of artificial intelligence.They trained a neural network on simulated galaxy mergers, then applied this model to galaxies observed in the cosmos.

Using an extremely large sample of approximately 8,000 accreting black-hole systems – which allowed the team to study the question in much more detail – it was found that mergers led to black-hole growth only in a very specific type of galaxies: star-forming galaxies containing significant amounts of cold gas.

She added: “On a clear night, you can just about spot this process happening in real-time with the Orion Nebula – a large, star-forming region in our galaxy and the closest of its kind to Earth – where you can see some stars that were formed recently and others that are still forming.”

 

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