A new model of the local universe reveals filaments of dark matter stretching across the cosmic void and linking galaxies.
Dark matter is the mysterious substance that makes up around 85% of the Universe’s matter content. Despite its ubiquitous nature and the fact that without its gravitational influence galaxies would literally fly apart — other than the fact that it is not composed of the baryons that make up the matter that surrounds us on an everyday basis — scientists still know very little about dark matter.
Yet, the nature of this non-baryonic matter is not the only secret it is likely to hold. Investigating dark matter deepens our knowledge of the Universe, its constituent galaxies, and how they evolved, and how they could end up.
Now new research reveals that dark matter, which forms a halo around most galaxies and can be mapped via its gravitational effects, may link galaxies in much more than a figurative way.
An international team of researchers has discovered by mapping the structure of the universe — or the ‘cosmic web’ — on a local level, that filamentary structures composed of dark matter could bridge the gaps between galaxies and link them together.
Ironically, it’s easier to study the distribution of dark matter much further away because it reflects the very distant past, which is much less complex. Over time, as the large-scale structure of the universe has grown, the complexity of the universe has increased, so it is inherently harder to make measurements about dark matter locally.
Donghui Jeong, associate professor of astronomy and astrophysics, Penn State
Joeng is one of the authors of a paper that will document the team’s map of the cosmic web set to be published online in Astrophysical Journal.
Mapping The Cosmic Web
Researchers know that dark matter forms the filamentary skeleton of the cosmic web that comprises the large-scale structure of the Universe, influencing the movement of galaxies and their contents.
Even though dark matter can’t be directly ‘seen’ as it doesn’t interact with the electromagnetic force, neither absorbing nor emitting photons, its presence can be inferred from its effects on another of the Universe’s fundamental forces; gravity.
Thus far, attempts to map the cosmic web have centered on creating a model of the early Universe and tracking its evolution over its 13.8 billion year existence. Whilst this intensive computing effort has created some impressive models of the Universe as a whole, the approach has struggled with the accurate mapping of the Universe on local scales.
This inspired Joeng and his colleagues to take an entirely new different approach to the mapping of the cosmic web. They used machine-learning that takes information regarding the motion and distribution of galaxies to build a model that predicts how dark matter is distributed.
In order to create and train their model, the team employed a large set of galaxy simulations from the IllustrisTNG project¹, which aims to illuminate the physical processes that drive galaxy formation and to understand when and how galaxies evolve into the structures that we observe today.
The simulations include data regarding the motion of galaxies and visible matter like gas and dust and as well as dark matter. From this wealth of information, the researchers selected galaxies similar to the Milky Way and then set about the properties of galaxies that are required to map dark matter distribution.
From there, the team was able to apply their model to real astronomical data collected from the region of the Universe around our galaxy obtained from the Cosmicflow-3 galaxy catalog² which details the movement of 17,000 galaxies around our own.
Bridges Between Galaxies
The success of the team’s cosmic map is visible in the accurate replication of well-studied structures in the local universe, including the region space containing the Milky way known as the ‘local sheet,’ the nearby galaxies defined as the ‘local group’, and even an empty region of space that neighbors the local group referred to as the ‘local void.’
What may stand out the most to cosmologists and astronomers in this new cosmic map are the new details that have not been well studied, including the filamentary structures that seem to link galaxies.
“Having a local map of the cosmic web opens up a new chapter of cosmological study,” explains Jeong. “We can study how the distribution of dark matter relates to other emission data, which will help us understand the nature of dark matter. And we can study these filamentary structures directly, these hidden bridges between galaxies.”
One of the aspects of local galactic evolution that the team’s model may be able to predict is the speculated approach of the Andromeda galaxy. It is currently believed that the Milky Way and its neighbor are slowly drawing towards each other, but it's unclear if this will result in a meeting and a merger between the two billions of years in the future. An investigation of the dark matter filaments that link the two galaxies could reveal how likely this merger is.
“Because dark matter dominates the dynamics of the universe, it basically determines our fate,” explains Jeong. “So we can ask a computer to evolve the map for billions of years to see what will happen in the local universe. And we can evolve the model back in time to understand the history of our cosmic neighborhood.”
The team says the next step for this research is to add more galaxies and hopefully improve the accuracy of their map. And the forthcoming surveys to be conducted at the James Webb Space Telescope and the Vera C. Rubin observatory could assist in this by providing observations of fainter galaxies as well as galaxies that are both smaller and further away.
References
1. IllustrisTNG project [https://www.tng-project.org/]
2. Tully. R., Courtois. H. M., Sorce. J. G., [2017], VizieR On-line Data Catalog: J/AJ/152/50, Haravrd, [https://ui.adsabs.harvard.edu/#abs/2017yCat..51520050T/abstract]
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.