Astronomers from the Cosmic Dawn Center have unveiled the nature of the densest region of galaxies seen with the James Webb Space Telescope (JWST) in the early Universe. They find it to be likely the progenitor of a massive, Milky Way-like galaxy, seen at a time when it is still assembling from smaller galaxies. The discovery corroborates our understanding of how galaxies form. 

According to our current understanding of structure formation in the Universe, galaxies form hierarchically, with small structures forming first in the very early Universe, later merging to build up larger structures. This is the prediction of theories and supercomputer simulations and is verified by observations of galaxies at various epochs in the history of the Universe.

To observe the very first structures assembling, we have to look as far back in time, and hence as far away, as possible. But these sources are both very small and very faint, and their detection requires advanced technologies.

In a new study, the early progenitor of what today will likely have evolved into a massive, Milky Way-sized galaxy, has been detected. This group of smaller galaxies, dubbed CGG-z5, was found through the observational program called "CEERS" with the James Webb Space Telescope and is seen when the Universe was only 1.1 billion years old, 8% of its current age.

CGG-z5 was discovered using the code GalCluster, which was created by Nikolaj Sillassen, an MSc student at the Cosmic Dawn Center (DAWN). 

"I developed the software during my studies to detect this kind of structure, and now we applied it to data from the CEERS program," says Nikolaj Sillassen, who already found a similar but more nearby group while testing the software.

"It's great to see how useful my code is becoming."

Impossible without James Webb

The brightest members of the galaxy group were discovered previously with the Hubble Space Telescope. But the CEERS program revealed new and smaller members.

"The other members of the group are both small and faint. Without the sensitivity and the spatial resolution of James Webb, we simply wouldn't be able to detect them," explains Shuowen Jin, Marie Curie Fellow at the Cosmic Dawn Center (DAWN) and lead author of the current study.

Exactly what the "future" of the galaxy group CGG-z5 will be, is of course unknown. Rather than forming a single galaxy, it could be that the group evolves into a large cluster of galaxies at later times. Yet another possibility is that the members are in reality not so closely packed as it seems, but instead a part of a filamentary structure that we just happen to view from one end to the other.

Help from supercomputer simulations

To distinguish between these scenarios, more precise observations involving the more time-consuming spectroscopy are needed. But in the meantime, help is available from supercomputer simulations:

"To better understand the nature and evolution of CGG-z5, we searched for similar structures in large-scale, hydrodynamical simulations," says Aswin Vijiayan, Postdoctoral Fellow at the Cosmic Dawn Center who conducted the simulation analysis in the study. "We found 14 structures that match closely the physical properties of our observed group CGG-z5, and then traced the evolution of these structures through time in the simulations, from the early Universe to the present epoch.

Although the exact unfolding of the evolution of these 14 structures is different, they all shared the same fate: Roughly 0.5 to 1 billion years later, they merge to form a single galaxy which, by the time the Universe is half its current age, have masses comparable to our own Milky Way.

"Given the predictions of the simulations, it is therefore tempting to speculate that the CGG-z5 system will also follow a similar evolutionary path and that we captured the process of small galaxies assembling into a single massive galaxy," Shuowen Jin concludes.

"Interestingly, the number of these early groups like CGG-z5 in a given volume of space is similar to the number of massive galaxies at later cosmic times", says Georgios Magdis, associate professor at DAWN and partaker in the study. "This makes merging groups appealing as the main progenitors of massive galaxies at later epochs".

Large samples and further work are needed to verify this picture.

A new AI algorithm developed by researchers at the Finnish Center for Artificial Intelligence is aimed at visualizing datasets as clearly as possible. The project demonstrated that the solution chosen independently by the algorithm was often very close to that most commonly favored by humans. 

The human brain has an astounding ability to observe various traits even from extremely large quantities of visual information. This ability is utilized, for example, in the study of large data masses whose content must be compacted into a form understandable to human intelligence. This problem of dimensional reduction is central to visual analytics.

At the Finnish Center for Artificial Intelligence (FCAI), researchers affiliated with Aalto University and the University of Helsinki tested the functionality of the most well-known methods of visual analytics, finding that none worked when the amount of data grew significantly. For example, the t-SNE, LargeViz, and UMAP methods were no longer able to distinguish extremely strong signals of observational groupings in the data when the number of observations was in the hundreds of thousands. 

Higgs boson data inspired the creation of the new algorithm

The dataset for experiments related to the discovery of the Higgs boson contains more than 11 million feature vectors, for instance.

“The visualizations drawn from them resembled a tangle of yarn, revealing none of the notable characteristics of particle behavior included in the data”, says Professor of Statistics and Probability Jukka Corander from the University of Helsinki.

“This finding provided the impetus to develop a new method that utilizes graphical acceleration similarly to modern AI methods for neural network computing. “

The AI algorithm designed by the researchers is aimed at visualization, so that data clusters and other macroscopic features, easily observed by and understandable to humans, are as distinct as possible. 

In the project, several volunteers tested the technique. It turned out that the solution independently chosen by the algorithm was often very close to the solution most typically favored by humans; in this situation, human intelligence clearly distinguishes, according to personal notions, between clusters of data composed of similar observations. When applying the technique to the Higgs boson data, their most important physical characteristics were clearly highlighted. 

“This is a veritable quantum leap in the field of visual analytics. Besides being several orders of magnitude faster than previous methods, our technique also is much more reliable in connection with challenging applications,” says Professor of Computer Science Jukka Corander from the University of Helsinki.

Under the direction of Corander’s group, a separate interface was also designed for utilizing the technique as efficiently as possible in genomics applications. This way, users can even analyze their datasets interactively by uploading files directly into the web browser. Employing global bacterial and SARS-CoV-2 datasets, this further study illustrated how the new tool can be used to quickly examine as many as millions of genomes and identify relevant characteristics.

The study was a collaboration between the Director of FCAI, Professor Sami Kaski, and Jukka Corander’s groups. Professor Zhirong Yang from the Norwegian University of Science and Technology served as the project lead. Professor Yang has a doctoral degree from Aalto University and has subsequently worked as a researcher at both Aalto University and the University of Helsinki in Professor Corander’s group.