Any Single Galaxy Reveals the Composition of an Entire Universe

A group of scientists may have discovered a revolutionary new approach to cosmology.

Cosmologists usually determine the universe’s composition by observing as much of it as they can. However, these researchers discovered that a machine learning program can examine a single simulated galaxy and estimate the entire nature of the digital universe in which it exists, a feat comparable to examining a random grain of sand under a microscope and calculating Eurasia’s mass. The robots appear to have discovered a pattern that could one day allow astronomers to draw broad conclusions about the real cosmos just by looking at its constituent parts.

“This is a fundamentally new theory,” said Francisco Villaescusa-Navarro, the work’s lead author and a theoretical astrophysicist at the Flatiron Institute in New York. “Rather than surveying all of these galaxies, you can just pick one.” It’s incredible that this works.”

That wasn’t intended to happen. The unlikely discovery stemmed from an experiment Villaescusa-Navarro offered to Jupiter Ding, a Princeton University undergraduate: build a neural network that can estimate a few of cosmological features given a galaxy’s parameters. The goal of the task was to get Ding acquainted with machine learning. Then they observed that the computer was right on the money with the total density of matter.
Villaescusa-Navarro stated, “I think the pupil made a mistake.” “To be honest, that was a little difficult for me to believe.”

The findings of the inquiry were published in a preprint on January 6 that was submitted for publication. The Cosmology and Astrophysics using Machine Learning Simulations (CAMELS) project created 2,000 digital worlds, which the researchers studied. These universes have a variety of compositions, ranging from 10% to 50% matter with the remainder made up of dark energy, which causes the universe to expand at an exponential rate. (Roughly one-third dark and visible matter and two-thirds dark energy make up our real cosmos.) Dark matter and visible matter swirled together into galaxies as the simulations proceeded. Complicated phenomena like as supernovas and jets erupting from supermassive black holes were also roughed out in the simulations.

Within these varied digital worlds, Ding’s neural network examined approximately 1 million simulated galaxies. It knew the size, composition, mass, and more than a dozen other properties of each galaxy from its godlike vantage point. It attempted to link the density of matter in the parent universe to this set of numbers.

It was a success. The neural network was able to forecast the cosmic density of matter to within 10% when tested on thousands of new galaxies from hundreds of universes it had never seen before. “It makes no difference whatever galaxy you’re thinking about,” Villaescusa-Navarro stated. “No one could have predicted this.”

“That one galaxy can increase [the density to] 10% or so, that was extremely astonishing to me,” said Volker Springel, a Max Planck Institute for Astrophysics specialist in modeling galaxy formation who was not involved in the study.

Because galaxies are inherently chaotic things, the algorithm’s performance astounded astronomers. Some grow all at once, while others feed on their neighbors. Supernovas and black holes in dwarf galaxies may expel most of their visible matter, but giant galaxies prefer to keep their mass. Despite this, each galaxy had managed to keep a careful eye on the general density of matter in its galaxy.

According to Pauline Barmby, an astronomer at Western University in Ontario, “the cosmos and/or galaxies are in some respects far simpler than we had assumed.” Another issue is that the simulations include defects that have gone unnoticed.

The researchers spent half a year attempting to figure out how the neural network had become so intelligent. They double-checked to make sure the algorithm wasn’t simply inferring density from the simulation’s code rather than the galaxies themselves. “Neural networks are really strong, but they are also quite inefficient,” Villaescusa-Navarro explained.

The researchers learned how the program calculated cosmic density through a series of tests. They honed focused on the most important qualities by continuously retraining the network while methodically hiding alternative cosmic properties.

A feature linked to a galaxy’s rotation speed, which correlates to how much matter (dark and otherwise) dwells in the galaxy’s center zone, was towards the top of the list. According to Springel, the discovery corroborates bodily intuition. Galaxies should become heavier and spin faster in a cosmos rich with dark matter. As a result, one may expect rotation speed to be related to cosmic matter density, however this link is far too shaky to be useful.

The neural network discovered a far more precise and intricate association between the matter density and 17 or so galaxy parameters. Despite galaxy mergers, star explosions, and black hole eruptions, this link continues. “You can’t plot it and squint at it by sight and detect the pattern until you get to more than [two attributes], but a neural network can,” said Shaun Hotchkiss, a cosmologist at the University of Auckland in New Zealand.

While the algorithm’s performance begs the issue of how many of the universe’s characteristics may be derived from a detailed examination of just one galaxy, cosmologists believe that practical applications will be restricted. Villaescusa-team Navarro’s discovered no trend when they tested their neural network on a different attribute, cosmic clumpiness. Other cosmic features, such as the accelerated expansion of the universe owing to dark energy, are expected to have minimal influence on individual galaxies, according to Springel.

In principle, an extensive examination of the Milky Way and maybe a few other neighboring galaxies could permit an extraordinarily exact calculation of our universe’s matter, according to the findings. According to Villaescusa-Navarro, such an experiment might provide information on other important numbers in the cosmos, such as the sum of the unknown masses of the universe’s three kinds of neutrinos.

However, in practice, the strategy would have to overcome a significant flaw. The CAMELS partnership uses two distinct methods to create its worlds. When given galaxies made according to one of the recipes, a neural network trained on that recipe generates poor density estimations. The neural network is discovering solutions that are unique to the rules of each recipe, as indicated by the cross-prediction failure. It would have no idea what to do with the Milky Way, a galaxy fashioned by real-world physics. Researchers will need to either make the simulations more realistic or adopt more generic machine learning techniques before deploying the methodology in the real world, which is a hefty task.

“I’m quite fascinated by the potential,” Springel added, “but one must not get carried away.”

However, Villaescusa-Navarro is encouraged by the neural network’s ability to detect patterns in the chaotic galaxies of two separate simulations. The digital finding raises the possibility that a comparable relationship between the great and tiny exists in the actual world.

He described it as “a very wonderful thing.” “It creates a link between the entire cosmos and a single galaxy,” says the author.

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