Artificial Intelligence Discovers an Equation for the Weight of Galaxy Clusters

Artificial Intelligence Discovers an Equation for the Weight of Galaxy Clusters
Artificial Intelligence Discovers an Equation for the Weight of Galaxy Clusters - New research has used an artificial tool to more accurately estimate the masses of galaxy clusters.

Astrophysicists at the Institute for Advanced Study, the Flatiron Institute, and their partners have developed a more accurate method for determining the mass of galactic massive clusters using artificial intelligence. Artificial intelligence has discovered that scientists can obtain better mass estimates than they had before by adding a simple term to an existing equation.

The new predictions will allow researchers to more precisely calculate the fundamental properties of the universe, according to astrophysicists who published their findings in the journal Proceedings of the National Academy of Sciences on March 17, 2023.

Francisco Villaescusa-Navarro, a research scientist at the Flatiron Institute's Center for Computational Astrophysics (CCA) in New York and co-author of the study, said, "It's a very simple thing; That's the beauty of the job," he says. “Although it is so simple, this term was not known before. People have been looking for this for decades but have yet to succeed.

The project was spearheaded by Digvijay Wadekar of the Institute for Advanced Study in Princeton, New Jersey, along with researchers from CCA, Princeton University, Cornell University, and the Harvard & Smithsonian Center for Astrophysics.

Knowing where and how much matter is will help you better understand the cosmos. The largest objects in the universe are galaxy clusters, which can contain hundreds to thousands of galaxies, as well as plasma, hot gas, and dark matter. These elements are bound together by the gravity of the cluster. Understanding such galaxy clusters is crucial in determining the beginning and ongoing evolution of the cosmos.

The total mass of a galaxy cluster is arguably the most important factor in defining its properties. This amount is difficult to estimate, however, because galaxies cannot be "weighed" by putting them on a balance. The invisibility of dark matter, which makes up a significant portion of a cluster's mass, adds to this challenge. Instead, they subtract the mass of a cluster from other measurable properties.

In the early 1970s, Rashid Sunyaev and Yakov B. Zel'dovich developed a new method for calculating the masses of galaxy clusters. Rashid Sunyaev is currently a distinguished visiting professor at the Institute for Advanced Study, School of Natural Sciences. Their approach is based on the idea that as matter is compressed by gravity, its electrons are pushed back.

The interaction between electrons and photons of light changes with this pressure on the electrons. The interaction produces new photons as photons from the Big Bang's afterlight strike the compressed material. The properties of these photons depend on how tightly the material is compressed by gravity, which in turn depends on the mass of the galaxy cluster. By counting the photons, astrophysicists can determine the mass of the cluster.

This "integrated electron pressure" is not a perfect substitute for mass, as changes in photon properties are dependent on the galaxy cluster. Wadekar and colleagues hypothesized that a machine learning technology called "symbolic regression" could reveal a more effective strategy. To determine which equation fits the data best, the program essentially tries various combinations of mathematical operators with different variables.

Wadekar and colleagues "fed" their AI algorithms with a cutting-edge universe simulation involving several galaxy clusters. Next, CCA research fellow Miles Cranmer used his algorithms to search for and identify other variables that would improve mass estimates.

AI is useful for discovering new combinations of parameters that human analysts might miss. For example, while detecting two key elements in a dataset is simple for human analysts, AI can better rank large volumes of data and often uncover unexpected contributing factors.

According to Wadekar, deep neural networks are currently the main focus of the machine learning community. These are incredibly strong, but one drawback is that they are almost completely opaque. What goes on inside them is beyond our comprehension. When something works well in physics, we want to know why. Symbolic regression is advantageous because it examines a given set of data and produces easy-to-understand mathematical expressions in the form of simple equations. It offers an easy-to-understand model.

The researchers' symbolic regression program added a single new term to the existing equation, giving them a new equation that could more accurately predict the mass of the galaxy cluster. Wadekar and colleagues then worked backwards from this artificial intelligence-generated equation to discover a physical explanation. They discovered that the presence of supermassive black holes at the centers of galaxies is correlated with other regions of galaxy clusters where mass inferences are less accurate. His revised equations improved mass estimates by reducing the importance of complex nuclei in calculations. The galaxy cluster is shaped like a donut.

The new formula eliminates the jelly in the center of the bun, which can cause greater inaccuracies, and instead focuses on the doughy edges for more accurate mass inferences.

The researchers tested the artificial intelligence-discovered equation on tens of thousands of simulated universes in CCA's CAMELS suite. They discovered that, compared to the equation currently used, the equation reduces the variability in galaxy cluster mass estimates by about 20 to 30 percent for large clusters.

The new equation could give observational astronomers a better understanding of the masses of objects they detect in upcoming galaxy cluster surveys. According to Wadekar, a series of studies targeting galaxy clusters are expected in the near future.

Simons Observatory, Stage 4 CMB experiment and eROSITA X-ray research are a few examples. By using the new equations we can increase the scientific return from these researches.

Wadekar also predicts that this work will be just the beginning of the use of symbolic regression in astrophysics. “We believe symbolic regression will be very useful in answering a variety of astrophysical questions,” Wadekar said. People in astronomy often make a linear fit between the two parameters and ignore all other factors. But these technologies enable you to do more today.

Using symbolic regression and other artificial intelligence methods to go beyond the limitations of currently existing two-parameter power laws, we can explore small astrophysical systems such as exoplanets down to galaxy clusters, the largest objects in the universe.


Günceleme: 24/03/2023 14:16

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