Skip to Content
Uncategorized

Simulating the Cosmic Web

The beautiful tapestry of filaments, sheets, and voids in the Cosmic Web is proving harder to model than anybody thought.

The idea that stars clump together in “island universes” is relatively new to astronomy. It was only in the 1920s and ’30s that astronomers agreed among themselves that “galaxies” must be separated by vast distances.

But only in the last 10 years or so have astronomers discovered that galaxies themselves form into a far larger structure. The 100 billion galaxies that we know about are woven into a wispy web-like arrangement consisting of dense compact clusters, elongated filaments and sheet-like walls, amid large near-empty void regions.

This structure has become known as the Cosmic Web and one of the great challenges in modern cosmology is to accurately model and simulate it.

That’s turning out to be tricky.

One of the important features of the Cosmic Web is that its structures range over many orders of magnitude. And since the largest structures, such as the wall-like features, are formed out of the smaller ones such as filaments and clusters, it’s crucial that any model can handle the relationship between them at all these scales.

That’s easier said than done. One way to imagine the problem is to think about zooming out from a particular cluster of galaxies to show the larger structures, rather in the manner of the famous Powers of Ten movie made in the 1970s.

As the small scale structures become too small to resolve, most computer models apply some kind of statistical smoothing process to make the large scale calculations easier.

But if you zoom back in again, there is no way to retrieve the information that is lost by the smoothing process, other than to rebuild the picture again from the original data.

That’s okay if all you want is a 3-D model of the universe. But it’s a problem if you want to simulate how the large scale structures form from smaller structures and how, in turn, the shape of the large structures influences the way smaller structures evolve.

This kind of feedback process is impossible to model when the smoothing process between different scales essentially destroys any meaningful links between them.

Enter Rien van de Weygaert and Willem Schaap at the University of Groningen in the Netherlands. These guys have developed a way of modeling structures over many scales without the unnatural smoothing that other approaches use.

Their trick is to think of galaxies as points in 3D space and to fill the space between them with tetrahedra. These tetrahedra must be constructed in such a way that, if a sphere were inflated inside each one until it touched the sides, there would be no galaxies inside each sphere.

This is known as a Delauney tessellation. What’s special about Delauney tessellations is that as the scale gets larger, there are rules for combining the tetrahedra into larger ones. These rules are special because they are reversible, meaning that the important features of the original structure can be reconstructed when you zoom in again.

That makes it much easier to simualte the feedback between structures on various scales.

So it’s no surprise that astronomers are excited about the potential of the so-called Delaunay Tessellation Field Estimator (DTFE). If you want to know more, de Weygaert and a few mates give a comprehensive outline of the idea on the arXiv today.

It should mean that we’ll have a much better model of the large scale structure of the universe.

It should also mean that we can update the Powers of Ten movie which, understandably given its age, shows no detail in the universe beyond our local cluster of galaxies.

Ref: arxiv.org/abs/0912.3448: Geometry and Morphology of the Cosmic Web: Analyzing Spatial Patterns in the Universe

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.