MSGID: 1:317/3 641d276d   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    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   
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   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.   
      
      
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   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.   
      
      
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   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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