AI and the Mass of Galaxy Clusters


The Coma Cluster contains more than 1,000 galaxies. Scientists have long been frustrated by large uncertainties in its mass. Credit: PSC 


A galaxy, as you may know, is a huge collection of stars that has a certain overall shape and which moves as a unit across the cosmos. Even larger groupings such as galaxy clusters exist. The individual galaxies in a cluster move about relative to each other and revolve around their common center of mass. Their velocities relative to our observation point can be gleaned from measurements of their red-shifts or blue-shifts.  Estimation of the mass of galaxy clusters is still fraught with uncertainties. It is not yet clear how to determine the three-dimensional structure of galaxy clusters which reduces further the confidence of estimating the mass. 

But why estimate the mass?

It is now known that stars in a galaxy revolve around the center of mass of the galaxy. But their velocities exceed the calculated velocity based on the detectable mass of the galaxy. This is true also of galaxy clusters. It is already generally accepted (although not settled) that to account for the missing mass, 'dark matter' exists which is thought to account for about 85% of the mass of the universe. 'Dark energy' is also thought to contribute to the invisible mass. In order to study these phenomena it is necessary to get estimates of mass of a galaxy cluster as precisely as possible. 

Calculating the mass of galaxy clusters using manual methods is time-consuming and human-intensive. This is where AI can help. A team of scientists from the Carnegies Mellon University trained an AI model on data from a simulated galaxy cluster in which the composition of all components was known. Then, the trained model was used to estimate the mass of the Coma galaxy cluster as a test. The mass of the Coma galaxy cluster was already estimated using human efforts. The AI output was found to agree with the human estimates. This approach is now a good candidate possibility for faster and more accurate assessments of the masses of galaxy clusters.  

The detailed research can be accessed here.


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