Artificial intelligence discovers secret equation for 'weighing' galaxy clusters

Date:
March 23, 2023
Source:
Simons Foundation
Summary:
Astrophysicists 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 have reported.


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FULL STORY ========================================================================== 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."
* RELATED_TOPICS
o Space_&_Time
# Galaxies # Astrophysics # Astronomy # Stars # Cosmology
# Black_Holes # Solar_Flare # Big_Bang
* RELATED_TERMS
o Dark_matter o Galaxy o Globular_cluster o
Large-scale_structure_of_the_cosmos o Dark_energy o Supergiant
o Open_cluster o Galaxy_formation_and_evolution

========================================================================== Story Source: Materials provided by Simons_Foundation. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro,
J. Colin
Hill, Miles Cranmer, David N. Spergel, Nicholas Battaglia,
Daniel Angle's-Alca'zar, Lars Hernquist, Shirley Ho. Augmenting
astrophysical scaling relations with machine learning: Application
to reducing the Sunyaev-Zeldovich flux-mass scatter. Proceedings
of the National Academy of Sciences, 2023; 120 (12) DOI:
10.1073/pnas.2202074120 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230323103405.htm

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