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Universe’s First Stars were Born in Small Clusters, Study Suggests

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In unveiling the nature of the first stars, the main astronomical clue is the elemental compositions of the second generation of stars, observed as extremely metal-poor stars, in the Milky Way. By using machine learning and state-of-the-art supernova nucleosynthesis, Dr. Tilman Hartwig from the University of Tokyo and the Kavli Institute for the Physics and Mathematics of the Universe and colleagues have now found the majority of observed second-generation stars were enriched by multiple supernovae.

Hartwig et al. shed a new light on solving the mystery of the first stars from the complex data set of Galactic archaeology surveys. Image credit: Kavli IPMU.

Big Bang nucleosynthesis has produced hydrogen, helium, and trace amounts of lithium in the first minutes of the Universe.

All heavier elements were synthesized and released by stars and their violent final fates, such as supernova explosions.

The crucial transition from a primordial Universe to a Universe enriched with heavier elements (summarized as ‘metals’ by astronomers) was initiated by the first stars.

Also termed Population III stars, these stars formed in pristine mini-halos around redshift 6-30.

They ended the cosmic dark ages, they provided the first metals, they contributed to the reionization of the Universe, they might have provided the seeds for the first supermassive black holes, and they have set the scene for all subsequent galaxy formation.

Despite their importance for cosmology and intensive studies in the last decades, only a little is known about the Population III 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,” Dr. Hartwig said.

“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.”

In their study, Dr. Hartwig and co-authors 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% of the observed extremely metal-poor stars have a chemical fingerprint consistent with enrichment by multiple previous supernovae.

“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 Professor Chiaki Kobayashi, an astronomer at the Kavli Institute for the Physics and Mathematics of the Universe and the University of Hertfordshire.

“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, is the best instrument to discover ancient stars in the outer regions of the Milky Way far beyond the solar neighborhood,” added Dr. Miho Ishigaki, an astronomer at the Kavli Institute for the Physics and Mathematics of the Universe, the National Astronomical Observatory of Japan and Tohoku University.

“The new algorithm 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,” Professor Kobayashi said.

The findings were published in the Astrophysical Journal.


Tilman Hartwig et al. 2023. Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data. ApJ 946, 20; doi: 10.3847/1538-4357/acbcc6

Source : Breaking Science News

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