AI improves cosmological parameter estimation, enhancing understanding of the universe’s expansion and structure.
The standard model of the universe depends on six numbers. Using a new method powered by artificial intelligence, researchers at the Flatiron Institute and their colleagues extracted information hidden in the distribution of galaxies to estimate the values of five of these cosmological parameters with unprecedented precision.
Compared to conventional techniques using the same galaxy data, the approach yielded less than half the uncertainty for the parameter describing the clumpiness of the universe’s matter. Additionally, the AI-powered method closely agreed with estimates of the cosmological parameters based on observations of other phenomena, such as the universe’s oldest light.
Their method, the Simulation-Based Inference of Galaxies (or SimBIG), was detailed in a series of recent papers, including a new study published in Nature Astronomy.
Enhancing Cosmological Understanding through SimBIG
Generating tighter constraints on the parameters while using the same data will be crucial to studying everything from the composition of dark matter to the nature of the dark energy driving the universe apart, says study co-author Shirley Ho, a group leader at the Flatiron Institute’s Center for Computational Astrophysics (CCA) in New York City. That’s especially true as new surveys of the cosmos come online over the next few years, she says.
The Value of Cosmological Surveys
“Each of these surveys costs hundreds of millions to billions of dollars,” Ho says. “The main reason these surveys exist is because we want to understand these cosmological parameters better. So if you think about it in a very practical sense, these parameters are worth tens of millions of dollars each. You want the best analysis you can to extract as much knowledge out of these surveys as possible and push the boundaries of our understanding of the universe.”
The six cosmological parameters describe the amount of ordinary matter, dark matter, and dark energy in the universe and the conditions following the Big Bang, such as the opacity of the newborn universe as it cooled and whether mass in the cosmos is spread out or in big clumps. The parameters “are essentially the ‘settings’ of the universe that determine how it operates on the largest scales,” says Liam Parker, co-author of the Nature Astronomy study and a research analyst at the CCA.
The Simulation-Based Inference of Galaxies, or SimBIG, allows astronomers to leverage artificial intelligence techniques to better estimate key properties of the universe. This video compares the distribution of galaxies in a simulated universe used to train SimBIG (right) to the galaxy distribution seen in the real universe (left). Credit: Bruno Régaldo-Saint Blancard/SimBIG collaboration
Leveraging AI for Deeper Cosmic Insights
One of the most important ways cosmologists calculate the parameters is by studying the clustering of the universe’s galaxies. Previously, these analyses only looked at the large-scale distribution of galaxies.
“We haven’t been able to go down to small scales,” says ChangHoon Hahn, an associate research scholar at Princeton University and lead author of the Nature Astronomy study. “For a couple of years now, we’ve known that there’s additional information there; we just didn’t have a good way of extracting it.”
AI-Driven Techniques and Future Applications
Hahn proposed a way to leverage AI to extract that small-scale information. His plan had two phases. First, he and his colleagues would train an AI model to determine the values of the cosmological parameters based on the appearance of simulated universes. Then they’d show their model actual galaxy distribution observations.
Hahn, Ho, Parker and their colleagues trained their model by showing it 2,000 box-shaped universes from the CCA-developed Quijote simulation suite, with each universe created using different values for the cosmological parameters. The researchers even made the 2,000 universes appear like data generated by galaxy surveys — including flaws from the atmosphere and the telescopes themselves — to give the model realistic practice. “That’s a large number of simulations, but it’s a manageable amount,” Hahn says. “If you didn’t have the machine learning, you’d need hundreds of thousands.”
By ingesting the simulations, the model learned over time how the values of the cosmological parameters correlate with small-scale differences in the clustering of galaxies, such as the distance between individual pairs of galaxies. SimBIG also learned how to extract information from the bigger-picture arrangement of the universe’s galaxies by looking at three or more galaxies at a time and analyzing the shapes created between them, like long, stretched triangles or squat equilateral triangles.
With the model trained, the researchers presented it with 109,636 real galaxies measured by the Baryon Oscillation Spectroscopic Survey. As they hoped, the model leveraged small-scale and large-scale details in the data to boost the precision of its cosmological parameter estimates. Those estimates were so precise that they were equivalent to a traditional analysis using around four times as many galaxies. That’s important, Ho says, because the universe only has so many galaxies. By getting higher precision with less data, SimBIG can push the limits of what’s possible.
One exciting application of that precision, Hahn says, will be the cosmological crisis known as the Hubble tension. The tension arises from mismatched estimates of the Hubble constant, which describes how quickly everything in the universe is spreading out.
Calculating the Hubble constant requires estimating the universe’s size using ‘cosmic rulers.’ Estimates based on the distance to exploding stars called supernovae in distant galaxies are around 10 percent higher than those based on the spacing of fluctuations in the universe’s oldest light.
New surveys coming online in the next few years will capture more of the universe’s history. Pairing data from those surveys with SimBIG will better reveal the extent of the Hubble tension, and whether the mismatch can be resolved or if it necessitates a revised model of the universe, Hahn says. “If we measure the quantities very precisely and can firmly say that there is a tension, that could reveal new physics about dark energy and the expansion of the universe,” he says.
Reference: “Cosmological constraints from non-Gaussian and nonlinear galaxy clustering using the SimBIG inference framework” by ChangHoon Hahn, Pablo Lemos, Liam Parker, Bruno Régaldo-Saint Blancard, Michael Eickenberg, Shirley Ho, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah and David Spergel, 21 August 2024, Nature Astronomy.
DOI: 10.1038/s41550-024-02344-2
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6 Comments
Dark matter and its energy seem to serve as a medium or field where normal matter resides (exists) and this medium manipulates the contents.
For better understanding of this universe, we may consider an overview of the infinite emptiness, so we compare mass zones with mass free zones in system of nature.
Dark matter and dark energy are two radically different mechanisms, though neither interacts much with light so they earn the moniker “dark”.
Dark matter seems to be particulate.
Dark energy is the vacuum energy density which now dominates the universe energy budget and hence now decides the space expansion rate.
The holographic principle derived from GR implies “space-time” does not work anywhere there is gravity.
Imaginary dark matter and dubious GPS lore apparently provide the best evidence that GR is correct.
Somehow the plucky and eminently believable British empire showed that dark matter is not needed for Mercury’s precession details, GR “predicted” it perfectly.
The holographic principle is not derived from general relativity but from string theory math. It also doesn’t apply to our flat space universe, since it would be the boundary of a de Sitter bulk space that we don’t see.
Not much of a surprise there, but nice to see LCDM being so robust (despite Hubble tension).
“For S8, the SimBIG B0 and CNN constraints are in good agreement with the weak lensing constraints and, thus, lower than the CMB constraints. Despite the differences, we emphasize that the SimBIG constraints are statistically consistent with both CMB and weak lensing.
For H0, the tighter constraints from SimBIG B0 and CNN are in good agreement with constraints from CMB and LSS. The B0 constraint is in excellent agreement with Planck and, thus, is in tension with the SH0ES measurement. The CNN constraint is notably higher than the B0 constraint and consequently reduces the tension with SH0ES. Nevertheless, it is statistically consistent
with B0 and Planck.”