From: Flatiron Institute Center for Computational Astrophysics
March 23, 2023 -- Astrophysicists
at the Institute for Advanced Study, the Flatiron Institute and their
colleagues have leveraged artificial intelligence to uncover a better way to
estimate the mass of colossal clusters of galaxies. The AI discovered that by
just adding a simple term to an existing equation, scientists can produce far
better mass estimates than they previously had.
The improved estimates
will enable scientists to calculate the fundamental properties of the universe
more accurately, the astrophysicists reported March 17, 2023, in
the Proceedings of the National Academy of Sciences.
“It’s such a simple
thing; that’s the beauty of this,” says study co-author Francisco
Villaescusa-Navarro, a research scientist at the Flatiron Institute’s Center
for Computational Astrophysics (CCA) in New York City. “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.”
The work was led by
Digvijay Wadekar of the Institute for Advanced Study in Princeton, New Jersey,
along with researchers from the CCA, Princeton University, Cornell University
and the Center for Astrophysics | Harvard & Smithsonian.
Understanding the
universe requires knowing where and how much stuff there is.
Galaxy clusters are the most massive objects in the universe: A single cluster
can contain anything from hundreds to thousands of galaxies, along with plasma,
hot gas and dark matter. The cluster’s gravity holds these components together.
Understanding such galaxy clusters is crucial to pinning down the origin and
continuing evolution of the universe.
Perhaps the most
crucial quantity determining the properties of a galaxy cluster is its total
mass. But measuring this quantity is difficult — galaxies cannot be ‘weighed’
by placing them on a scale. The problem is further complicated because the dark
matter that makes up much of a cluster’s mass is invisible. Instead, scientists
deduce the mass of a cluster from other observable quantities.
In the early 1970s,
Rashid Sunyaev, current distinguished visiting professor at the Institute for
Advanced Study’s School of Natural Sciences, and his collaborator Yakov B.
Zel’dovich developed a new way to estimate galaxy cluster masses. Their method
relies on the fact that as gravity squashes matter together, the matter’s
electrons push back. That electron pressure alters how the electrons interact
with particles of light called photons. As photons left over from the Big
Bang’s afterglow hit the squeezed material, the interaction creates new
photons. The properties of those photons depend on how strongly gravity is
compressing the material, which in turn depends on the galaxy cluster’s heft.
By measuring the photons, astrophysicists can estimate the cluster’s mass.
However, this
‘integrated electron pressure’ is not a perfect proxy for mass, because the
changes in the photon properties vary depending on the galaxy cluster. Wadekar
and his colleagues thought an artificial intelligence tool called ‘symbolic
regression’ might find a better approach. The tool essentially tries out
different combinations of mathematical operators — such as addition and
subtraction — with various variables, to see what equation best matches the
data.
Wadekar and his
collaborators ‘fed’ their AI program a state-of-the-art universe simulation
containing many galaxy clusters. Next, their program, written by CCA research
fellow Miles Cranmer, searched for and identified additional variables that
might make the mass estimates more accurate.
AI is useful for
identifying new parameter combinations that human analysts might overlook. For
example, while it is easy for human analysts to identify two significant
parameters in a dataset, AI can better parse through high volumes, often
revealing unexpected influencing factors.
“Right now, a lot of
the machine-learning community focuses on deep neural networks,” Wadekar
explained. “These are very powerful, but the drawback is that they are almost
like a black box. We cannot understand what goes on in them. In physics, if
something is giving good results, we want to know why it is doing so. Symbolic
regression is beneficial because it searches a given dataset and generates
simple mathematical expressions in the form of simple equations that you can
understand. It provides an easily interpretable model.”
The researchers’
symbolic regression program handed them a new equation, which was able to
better predict the mass of the galaxy cluster by adding a single new term to
the existing equation. Wadekar and his collaborators then worked backward from
this AI-generated equation and found a physical explanation. They realized that
gas concentration correlates with the regions of galaxy clusters where mass
inferences are less reliable, such as the cores of galaxies where supermassive
black holes lurk. Their new equation improved mass inferences by downplaying
the importance of those complex cores in the calculations. In a sense, the
galaxy cluster is like a spherical doughnut. The new equation extracts the
jelly at the center of the doughnut that can introduce larger errors, and
instead concentrates on the doughy outskirts for more reliable mass inferences.
The researchers tested
the AI-discovered equation on thousands of simulated universes from the
CCA’s CAMELS suite. They found that the equation reduced the variability in
galaxy cluster mass estimates by around 20 to 30 percent for large clusters
compared with the currently used equation.
The new equation can
provide observational astronomers engaged in upcoming galaxy cluster surveys
with better insights into the mass of the objects they observe. “There are
quite a few surveys targeting galaxy clusters [that] are planned in the near
future,” Wadekar noted. “Examples include the Simons Observatory, the Stage 4
CMB experiment and an X-ray survey called eROSITA. The new equations can help
us in maximizing the scientific return from these surveys.”
Wadekar also hopes that
this publication will be just the tip of the iceberg when it comes to using
symbolic regression in astrophysics. “We think that symbolic regression is
highly applicable to answering many astrophysical questions,” he said. “In a
lot of cases in astronomy, people make a linear fit between two parameters and
ignore everything else. 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.”
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