A team of researchers discovered that the majority of observed second-generation stars in the universe were enriched by several supernovae utilizing machine learning and cutting-edge supernova nucleosynthesis, according to a recent study published in The Astrophysical Journal.
A schematic illustration of the first star’s supernovae and observed spectra of extremely metal-poor stars. Ejecta from the supernovae enrich pristine hydrogen and helium gas with heavy elements in the universe (cyan, green, and purple objects surrounded by clouds of ejected material). If the first stars are born as a multiple stellar system rather than as an isolated single star, elements ejected by the supernovae are mixed together and incorporated into the next generation of stars. The characteristic chemical abundances in such a mechanism are preserved in the atmosphere of the long-lived low-mass stars observed in our Milky Way Galaxy. The team invented the machine learning algorithm to distinguish whether the observed stars were formed out of ejecta of a single (small red stars) or multiple (small blue stars) previous supernovae, based on measured elemental abundances from the spectra of the stars. Image Credit: Kavli IPMU
Nuclear astrophysics research has revealed that atoms heavier than carbon are generated in the cosmos by stars. However, the first stars, those created just after the Big Bang, did not contain such heavy elements as metals.
The following generation of stars included just a trace of the heavy elements created by the first stars. Researchers must examine these metal-poor stars to comprehend the universe in its early stages.
Fortunately, these second-generation metal-poor stars have been detected in the Milky Way Galaxy and investigated by a team of Affiliate Members of the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) to narrow in on the physical characteristics of the universe’s first stars.
The team was led by Kavli IPMU Visiting Associate Scientist and the University of Tokyo Institute for Physics of Intelligence Assistant Professor Tilman Hartwig and included 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.
They employed artificial intelligence to analyze elemental abundances in almost 450 of the most metal-poor stars ever recorded. They discovered that 68% of the reported highly metal-poor stars exhibit a chemical fingerprint compatible with enrichment by numerous previous supernovae, using a newly developed supervised machine learning technique trained on theoretical supernova nucleosynthesis models.
The team’s findings provide the first quantifiable constraint based on observations of the first stars’ multiplicity.
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.
Tilman Hartwig, Study Lead Author and Assistant Professor, Institute for Physics of Intelligence, School of Science, The University of Tokyo
Kobayashi, also a Leverhulme Research Fellow, added, “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 further stated, “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 innovative method developed in this study paves the way for making the most of diverse chemical fingerprints identified by the Prime Focus Spectrograph in metal-poor stars.
Kobayashi concluded, “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.”
Journal Reference
Hartwig, T., et al. (2023) Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data. The Astrophysical Journal. doi:10.3847/1538-4357/acbcc6.