What makes black holes grow and create new stars? | Urania

New simulations show that more than just galaxy collisions is needed for black holes to grow and star formation.

Supermassive black holesWhen active, they play a major role in development Galaxies. Until now, it was believed that this increase was the result of violent collisions between two galaxies, followed by their merger. However, new research by scientists at the University of Bath suggests that galaxy mergers alone are not enough to power a black hole. A reservoir of cold gas at the center of the host galaxy is also necessary.

new study, published In the Monthly Notices of the Royal Astronomical Society, it is believed to have been the first to use Machine learning To classify galaxy mergers for the specific purpose of studying the relationship between galaxy mergers, the accumulation of supermassive black holes, and star formation. Until now, mergers have been classified (often incorrectly) based only on human observations.

When people look for galaxy mergers, they don't always know what they're looking at and often use intuition to judge whether a galaxy merger has occurred. – said Matilda Averett MacKenzie, a PhD student in the Department of Physics at the University of Bath and first author of the research article. The study was the result of cooperation between partners from BiD4BEST (Big Data Applications for Black Hole Evolution Studies)whose innovative training network provides PhD training in the formation of supermassive black holes.

She added: By training the machine to classify mergers, you can get a truer picture of what galaxies are actually doing.

Supermassive black holes
Supermassive black holes are found at the centers of all massive galaxies (to give you an idea of ​​the size, milky way, which contains about 200 billion stars, is only a medium-sized galaxy). The masses of these supermassive black holes typically range from millions to billions of solar masses.

For most of their lives, these black holes are quiet and inactive as matter swirls around them, and have little impact on the galaxy as a whole. However, during the short phases of their lives (only short on the astronomical scale and likely lasting millions to hundreds of millions of years), they use gravitational forces to attract large amounts of gas to themselves (an event known as Accumulation), resulting in the appearance of a bright disk that can outshine the entire galaxy.

These short phases of activity are crucial for the evolution of galaxies because the huge amounts of energy released during accretion can affect star formation in galaxies. Therefore, determining what causes a galaxy to switch between two states – rest and star formation – is one of the biggest challenges in astrophysics.

Determining the role of supermassive black holes in the evolution of galaxies is crucial to our study of the universe – Averett McKenzie said,

Human inspection and machine learning
For decades, theoretical models have suggested that black holes grow as galaxies merge. But astrophysicists studying the relationship between galaxy mergers and black hole growth have questioned these models for many years, asking a simple question: How can galaxy mergers be reliably determined?

The most commonly used method was visual inspection. Classifiers – experts or outsiders – observe galaxies and identify significant asymmetries or long tidal tails (thin, elongated regions of stars and interstellar gas extending out into space) associated with merging galaxies.

However, this method of observation is time-consuming and unreliable because people easily make mistakes in their ratings. As a result, fusion studies often produce conflicting results.

In a new study led by Bath, scientists challenged themselves to improve how mergers are classified by examining the relationship between black hole growth and galaxy evolution using artificial intelligence.

Inspired by the human brain
They trained a neural network (a subset of machine learning inspired by the human brain that mimics the way biological neurons transmit signals to each other) to simulate galaxy mergers, and then applied this model to analyze observed galaxies in space.

In this way, they were able to identify mergers without human bias and investigate the relationship between galaxy mergers and black hole growth. They showed that the neural network outperforms human classifiers in identifying mergers, and in fact, human classifiers tend to confuse normal galaxies with mergers.

Using this new methodology, scientists were able to prove that mergers are not strongly linked to the growth of black holes. Merger signatures are equally common in galaxies that contain supermassive black holes or do not accrete.

By analyzing a very large data set of about 8,000 black hole accretion systems, which allowed the team to study in much greater detail, they found that mergers led to the growth of black holes only in a very specific type of galaxy: Star-forming galaxies It contains large amounts of cold gas.

This shows that galaxy mergers alone are not enough to power black holes: large amounts of cold gas must also be present for a black hole to grow.

Mrs. Averett McKenzie said: In order for galaxies to form stars, they must contain clouds of cold gas that can collapse to form stars. High-energy processes, such as accretion of supermassive black holes, heat this gas, making it too energetic to collapse or be blown out of the galaxy.

She added: On a clear night you can see this process in real time Orion Nebula – A large star formation region Our galaxy And the closest to Earth – where you can see some stars that have recently formed and others that are still forming.

“Until now everyone has been studying mergers in the same way – through visual classification,” said Dr Caroline Filforth, senior lecturer in the Department of Physics and Ms Averett MacKenzie's supervisor at the University of Bath. Using this method, and using specialized classifiers that can detect more subtle features, we were only able to look at a few hundred galaxies, no more.

Instead, the use of machine learning opens up a whole new and very exciting field in which thousands of galaxies can be analyzed simultaneously. You can get consistent results over very large samples, and you can look at many different properties of the black hole at any given time.

development:
Agnieszka Novak

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Source: University of Bath

Pictured: A pair of disc galaxies in the final stages of merging. Source: NASA