Researchers use AI to discover new planet outside solar system
The exoplanet was detected using machine learning, a branch of artificial intelligence

Date:
April 24, 2023
Source:
University of Georgia
Summary:
A research team has confirmed evidence of a previously unknown
planet outside of our solar system, and they used machine learning
tools to detect it. A recent study by the team showed that machine
learning can correctly determine if an exoplanet is present by
looking in protoplanetary disks, the gas around newly formed
stars. The newly published findings represent a first step toward
using machine learning to identify previously overlooked exoplanets.


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FULL STORY ==========================================================================
A University of Georgia research team has confirmed evidence of a
previously unknown planet outside of our solar system, and they used
machine learning tools to detect it.

A recent study by the team showed that machine learning can correctly
determine if an exoplanet is present by looking in protoplanetary disks,
the gas around newly formed stars.

The newly published findings represent a first step toward using machine learning to identify previously overlooked exoplanets.

"We confirmed the planet using traditional techniques, but our models
directed us to run those simulations and showed us exactly where the
planet might be," said Jason Terry, doctoral student in the UGA Franklin College of Arts and Sciences department of physics and astronomy and
lead author on the study.

"When we applied our models to a set of older observations, they
identified a disk that wasn't known to have a planet despite having
already been analyzed.

Like previous discoveries, we ran simulations of the disk and found
that a planet could re-create the observation." According to Terry,
the models suggested a planet's presence, indicated by several images
that strongly highlighted a particular region of the disk that turned
out to have the characteristic sign of a planet -- an unusual deviation
in the velocity of the gas near the planet.

"This is an incredibly exciting proof of concept. We knew from our
previous work that we could use machine learning to find known forming exoplanets," said Cassandra Hall, assistant professor of computational astrophysics and principal investigator of the Exoplanet and Planet
Formation Research Group at UGA. "Now, we know for sure that we can use
it to make brand new discoveries." The discovery highlights how machine learning has the power to enhance scientists' work, utilizing artificial intelligence as an added tool to expand researchers' accuracy and more efficiently economize their time when engaged in such a vast endeavor
as investigating deep, outer space.

The models were able to detect a signal in data that people had already analyzed; they found something that previously had gone undetected.

"This demonstrates that our models -- and machine learning in general --
have the ability to quickly and accurately identify important information
that people can miss. This has the potential to dramatically speed up
analysis and subsequent theoretical insights," Terry said. "It only took
about an hour to analyze that entire catalog and find strong evidence for
a new planet in a specific spot, so we think there will be an important
place for these types of techniques as our datasets get even larger."
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========================================================================== Story Source: Materials provided by University_of_Georgia. Original
written by Alan Flurry.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. J. P. Terry, C. Hall, S. Abreau, S. Gleyzer. Kinematic Evidence
of an
Embedded Protoplanet in HD 142666 Identified by Machine
Learning. The Astrophysical Journal, 2023; 947 (2): 60 DOI:
10.3847/1538-4357/acc737 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/04/230424133426.htm

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