In a major breakthrough in astronomy, scientists from the University of Bath used artificial intelligence technology to uncover the important impact of supermassive black holes on the evolution of the galaxy and discovered new mysteries of black hole growth. Traditionally, it is believed that the collision and merger of two galaxies triggered the growth of black holes. However, new research shows that the merger of galaxies alone is not enough to stimulate black holes. There must also be a large amount of cold gas in the center of the galaxy.
This study is the first to use machine learning to classify galactic mergers, with the aim of exploring the relationship between “galactic mergers”, “accretion of supermassive black holes” and “star formation”. In the past, most of these classification tasks relied on human observation, which was not only time-consuming but also error-prone.
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The scientists trained a neural network model to simulate galactic mergers and then applied the model to actual observed galaxies. They found that this approach was more accurate at identifying merged events than manual classification and avoided human bias.
Formation conditions: A galaxy containing large amounts of cold gas and star formation
Seyfert 2 galaxies, including their stellar masses (shown in the chart above) and[O iii]The relationship between luminosity (i.e., the luminous intensity of oxygen ions) and redshift (the stretching of light wavelengths caused by the expansion of the universe). The histograms in the graph (M* and z histograms) illustrate the grouping method used for matching the control samples. It is worth noting that the control sample matches the main sample in terms of stellar mass and redshift, so the histograms for the control group are identical and are not shown separately here. (Photo / “Monthly Notices of the Royal Astronomical Society”) Advertisement (Please continue reading this article)
The research team found that galactic mergers alone are not enough to stimulate the growth of black holes. They further studied about 8,000 accreting black hole systems and found that only in certain types of galaxies—those with large amounts of cold gas and stars forming—mergers led to the growth of black holes.
This discovery is not only crucial to understanding the role of supermassive black holes in the evolution of the Milky Way, but also provides astronomers with new research methods. Dr Caroline Wilforth, a physicist at the University of Bath, said that using machine learning we can analyze thousands of galaxies, get consistent results and observe many different properties of black holes simultaneously.
This research not only challenges the long-standing astronomical assumption that particles with the same charge can attract each other under certain conditions, but also provides new perspectives to understand and apply molecular interactions. In the pharmaceutical field, this helps improve drug stability and formulation. In treating diseases such as neurodegeneration, it provides insights into the mechanisms of abnormal molecular aggregation, thereby facilitating the development of new treatment strategies.
The findings, published in the journal Monthly Notices of the Royal Astronomical Society, open a new chapter in astronomy and physics.
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First picture source: NASA cc By4.0
Image Source:Depositphotos cc By4.0
Reference papers:
1.A post-merger enhancement only in star-forming Type 2 Seyfert galaxies: the deep learning viewMonthly Notices of the Royal Astronomical Society
Further reading:
1.Quantum secrets revealed: Physicists discover magical charge dance in quantum materials