Using machine learning and state-of-the-art supernova nucleosynthesis, a team of researchers discovered that several supernovae have enriched most second-generation stars in the universe. Their research is published in The Astrophysical Journal.
Stars born shortly after the Big Bang did not contain metals, which astronomers label as “metals,” although those elements heavier than carbon and including carbon appear to be created by stars. This has been demonstrated by research into nuclear astrophysics.
From the first stars, only a tiny quantity of heavy elements was acquired by the upcoming generation of stars. Researchers must study these metal-deficient stars to comprehend the universe’s early days.
The Kavli IPMU team has conducted a study of second-generation metal-poor stars observed in the Milky Way galaxy, which allows them to gain an insight into the physical properties of the first stars that populated the universe. Thankfully, these stars are plentiful enough to enable such exploration.
Using artificial intelligence, a team of scientists led by Kavli IPMU Visiting Associate Scientist and The University of Tokyo Institute for Physics of Intelligence Assistant Professor Tilman Hartwig and several other visiting and senior scientists analyzed the elemental abundances of over 450 stars that are extremely metal-poor.
Using a newly developed supervised machine learning algorithm trained on theoretical supernova nucleosynthesis models, it has been found that the chemical fingerprint of 68% of the observed extremely metal-poor stars is consistent with enrichment by multiple previous supernovae.
The team’s results provide the first quantitative constraint based on observational data on the diversity of the first stars.
Hartwig says:
“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,”
“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.”
Kobayashi says:
“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”
Ishigaki says:
“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,”
The Prime Focus Spectrograph’s detection of metal-poor stars has enabled a new algorithm that allows them to use their varied chemical fingerprints best. This algorithm provides great opportunities for further exploration.
Kobayashi went on to say:
“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”
Machine learning, as demonstrated in a recent study by Tilman Hartwig et al. (2023), can detect the diversity of the first stars in stellar archaeology data. The results of this research have now been published in The Astrophysical Journal.
Subject to copyright, no part of this document may be reproduced without written consent. Everything else is prohibited besides being used for private study or research. Information contained in this document should only be used for reference purposes.
AI technology in astrophysics also raises important ethical and philosophical questions. As with any technology that seeks to uncover the mysteries of the universe, it is crucial to consider this research’s potential implications and consequences.
Overall, using AI technology in astrophysics represents an exciting new frontier in scientific research. As our understanding of the universe continues to expand, AI technology will undoubtedly play an increasingly important role in helping us unlock the secrets of the cosmos.
Source: phys.org
