AI finds the first stars were not alone

Date:
March 23, 2023
Source:
Kavli Institute for the Physics and Mathematics of the Universe
Summary:
Machine learning and state-of-the-art supernova nucleosynthesis
has helped researchers find that the majority of observed
second-generation stars in the universe were enriched by multiple
supernovae.


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FULL STORY ==========================================================================
By using machine learning and state-of-the-art supernova
nucleosynthesis, a team of researchers have found the majority of
observed second-generation stars in the universe were enriched by multiple supernovae, reports a new study in The Astrophysical Journal.


========================================================================== Nuclear astrophysics research has shown elements including and heavier
than carbon in the universe are produced in stars. But the first stars,
stars born soon after the Big Bang, did not contain such heavy elements,
which astronomers call 'metals'. The next generation of stars contained
only a small amount of heavy elements produced by the first stars. To understand the universe in its infancy, it requires researchers to study
these metal-poor stars.

Luckily, these second-generation metal-poor stars are observed in our
Milky Way Galaxy, and have been studied by a team of Affiliate Members
of the Kavli Institute for the Physics and Mathematics of the Universe
(Kavli IPMU) to close in on the physical properties of the first stars
in the universe.

The team, led by Kavli IPMU Visiting Associate Scientist and The
University of Tokyo Institute for Physics of Intelligence Assistant
Professor Tilman Hartwig, including Visiting Associate Scientist and
National Astronomical Observatory of Japan Assistant Professor Miho
Ishigaki, Visiting Senior Scientist and University of Hertfordshire
Professor Chiaki Kobayashi, Visiting Senior Scientist and National
Astronomical Observatory of Japan Professor Nozomu Tominaga, and Visiting Senior Scientist and The University of Tokyo Professor Emeritus Ken'ichi Nomoto, used artificial intelligence to analyze elemental abundances
in more than 450 extremely metal-poor stars observed to date. Based on
the newly developed supervised machine learning algorithm trained on theoretical supernova nucleosynthesis models, they found that 68 per cent
of the observed extremely metal-poor stars have a chemical fingerprint consistent with enrichment by multiple previous supernovae.

The team's results give the first quantitative constraint based on
observations on the multiplicity of the first stars.

"Multiplicity of the first stars were only predicted from numerical
simulations so far, and there was no way to observationally examine the theoretical prediction until now," said lead author Hartwig. "Our result suggests that most first stars formed in small clusters so that multiple
of their supernovae can contribute to the metal enrichment of the early interstellar medium," he said.

"Our new algorithm provides an excellent tool to interpret the big data we
will have in the next decade from on-going and future astronomical surveys across the world" said Kobayashi, also a Leverhulme Research Fellow.

"At the moment, the available data of old stars are the tip of the
iceberg within the solar neighborhood. The Prime Focus Spectrograph,
a cutting-edge multi-object spectrograph on the Subaru Telescope
developed by the international collaboration led by Kavli IPMU, is the
best instrument to discover ancient stars in the outer regions of the
Milky Way far beyond the solar neighborhood.," said Ishigaki.

The new algorithm invented in this study opens the door to make the most
of diverse chemical fingerprints in metal-poor stars discovered by the
Prime Focus Spectrograph.

"The theory of the first stars tells us that the first stars should be
more massive than the Sun. The natural expectation was that the first
star was born in a gas cloud containing the mass million times more than
the Sun. However, our new finding strongly suggests that the first stars
were not born alone, but instead formed as a part of a star cluster or
a binary or multiple star system.

This also means that we can expect gravitational waves from the first
binary stars soon after the Big Bang, which could be detected future
missions in space or on the Moon," said Kobayashi.

* RELATED_TOPICS
o Space_&_Time
# Stars # Astrophysics # Galaxies # Nebulae
o Matter_&_Energy
# Physics # Chemistry # Inorganic_Chemistry #
Quantum_Physics
* RELATED_TERMS
o Supernova o Nucleosynthesis o Nuclear_fusion
o Big_Bang_nucleosynthesis o Multiverse o
Galaxy_formation_and_evolution o Big_Bang o Planetary_nebula

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


========================================================================== Related Multimedia:
* Schematic_illustration_of_the_first_star's_supernovae ========================================================================== Journal Reference:
1. Tilman Hartwig, Miho N. Ishigaki, Chiaki Kobayashi, Nozomu Tominaga,
Ken'ichi Nomoto. Machine Learning Detects Multiplicity of the
First Stars in Stellar Archaeology Data. The Astrophysical Journal,
2023; 946 (1): 20 DOI: 10.3847/1538-4357/acbcc6 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230323103350.htm

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