There’s no scale large enough in the universe to ‘weigh’ a cluster of galaxies. To find out their mass – much of which is the elusive dark matter – scientists must estimate based on particular observations.
But a new paper published in PNAS has outlined how artificial intelligence was able to uncover a simple tweak that allows a better estimate of these huge galaxy clusters’ mass.
“It’s such a simple thing; that’s the beauty of this,” says study co-author Francisco Villaescusa-Navarro, a computational astrophysicist at the Flatiron Institute.
“Even though it’s so simple, nobody before found this term. People have been working on this for decades, and still they were not able to find this.”
This important, because understanding these galaxy clusters is vital to understanding the evolution of our Universe.
Measuring total mass is difficult though, particularly because dark matter – the stuff that makes up most of the cluster’s mass – is invisible.
The current method works because as gravity squashes matter together, the matter’s electrons push back. This changes the way electrons act with photons, and by measuring these photons, scientists can estimate the mass of the galaxy cluster.
But when the new researchers used an AI tool called “symbolic regression” to try and come up with a better, more accurate method, the AI came up with an equation with a new term added.
This new term focuses the analysis less on the cores of galaxies, and more on the reliable outer regions. This means that the AI equation was able to better predict the mass of the clusters.
“In a lot of cases in astronomy, people make a linear fit between two parameters and ignore everything else,” says first author Digvijay Wadekar, from the Institute for Advanced Study.
“But nowadays, with these tools, you can go further. Symbolic regression and other artificial intelligence tools can help us go beyond existing two-parameter power laws in a variety of different ways, ranging from investigating small astrophysical systems like exoplanets, to galaxy clusters, the biggest things in the universe.”
The research has been published in PNAS.