AI Reveals First Stars Were Formed in Clusters, Not Born Alone [Study]

Stars are not isolated from the start. According to a new study using artificial intelligence (AI), they already came in clusters from the beginning.

Stars Are Not Born Alone

A new study used machine learning and advanced supernova nucleosynthesis. The researchers have learned that multiple supernovae enriched the majority of the second-generation stars in the universe.

The first stars born after Big Bang did not contain heavy elements, which astronomers call "metals." The next generation of stars had a small number of heavy elements produced by the first stars, Science Daily reported.

Researchers want to study the metal-poor stars to understand how the universe evolves. Fortunately, the second-generation metal-poor stars are observed in the 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.

According to the theory of the first stars, the first stars should be bigger than the Sun, and the natural expectation was that the first star was born in a gas cloud with a mass million times greater than the Sun. However, the new finding suggests that the first stars were not born alone but rather formed as a part of a star cluster or a binary or multiple-star system. It also means we can expect gravitational waves from the first binary stars soon after Big Bang.

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How Did the Research Go?

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 the 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, utilized artificial intelligence to analyze elemental abundances in more than 450 extremely metal-poor stars that they observed.

They discovered that 68% of the observed highly metal-poor stars had a chemical fingerprint consistent with enrichment by several prior supernovae based on the newly built supervised machine learning technique trained on theoretical supernova nucleosynthesis models.

The team's findings provide the first quantifiable restriction based on observations of the earliest stars' multiplicity.

According to lead author Hartwig, the multiplicity of the early stars has only previously been anticipated through numerical simulations because there has previously been no method to test the theoretical prediction. The findings revealed that most first stars formed in tiny clusters, so several of their supernovae might contribute to the early interstellar medium's metal enrichment.

According to Kobayashi, also a Leverhulme Research Fellow, the new algorithm gives an ideal tool to analyze the huge data they will have in the coming decade from ongoing and prospective astronomical surveys worldwide.

With the aid of the novel method developed in this study, it is now possible to maximize the variety of chemical fingerprints in metal-poor stars identified by the Prime Focus Spectrograph.

The study "Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data" is published in The Astrophysical Journal.