MSGID: 1:317/3 6447576c
PID: hpt/lnx 1.9.0-cur 2019-01-08
TID: hpt/lnx 1.9.0-cur 2019-01-08
 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
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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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