AI discovers the universe’s first star wasn’t lonely

Machine Learning

AI discovers that the first stars were clustered together

The first supernova ejection (cyan, green, and purple objects surrounded by clouds of ejected material) enriches primordial hydrogen and helium gas with heavy elements. If the first stars were born as multiple star systems rather than as isolated single stars, the elements ejected by the various supernovae would mix and be incorporated into the next generation of stars. Chemical abundances characteristic of such mechanisms are conserved in the atmospheres of long-lived stars. The team identified stars formed from the ejecta of a single observed supernova (shown in red in the diagram) and stars formed from ejecta from multiple supernovae (shown in blue in the diagram). invented a machine learning algorithm to distinguish between We measured the abundance of elements from the star’s spectrum. Credit: Kavli IPMU

Using artificial intelligence, an international team analyzed the chemical composition of extremely metal-poor stars and found that the first stars in the universe were likely born in groups rather than individually. The method will be applied to future observations to better understand the early Universe.

An international team has used artificial intelligence to analyze the chemical abundance of old stars and found signs that the first stars in the universe were born in groups rather than as isolated single stars. The team now hopes to apply the method to new data from ongoing and planned observational surveys to better understand the early universe.

Since the Big Bang, hydrogen, helium, and lithium are the only elements in the universe. Most of the other elements that make up the world around us were produced by nuclear reactions in stars. Some elements are formed by nuclear fusion in the stellar core, while others are formed in the star’s explosive supernova death. Supernovae also play a key role in scattering star-made elements so that they can be incorporated into the next generation of stars, planets, and even life.

The first generation of stars, the first to produce elements heavier than lithium, is of particular interest. However, first-generation stars are difficult to study because they have never been observed directly. Both are believed to have already exploded as supernovae. Instead, researchers are trying to draw inferences about first-generation stars by studying the chemical signatures of first-generation supernovae imprinted on the stars of the next generation. Based on their composition, extremely metal-poor stars are thought to be stars that formed after the first supernova explosion. Very metal-poor stars are rare, but enough are now being discovered to be analyzed as a group.

In this study, a team, including members from the University of Tokyo/Kavli IPMU, the National Astronomical Observatory of Japan, and the University of Hertfordshire, used artificial intelligence to identify more than 450 telescopically observed extremely metal-poor stars. We took a new approach to interpret elemental abundances. Including the Subaru Telescope. They found that 68% of the observed extremely metal-poor stars have chemical fingerprints consistent with previous supernova enrichment.

Supernovae must have occurred in close proximity for ejecta from previous supernovae to mix into one star. This means that in many cases first-generation stars must have formed together as clusters rather than as isolated stars. This provides the first quantitative constraint based on observations of first star multiplicity.

Now the team hopes to apply the method to big data from current and future observational programs, such as the data expected from the prime focus spectrograph of the Subaru Telescope.

These results appeared as Hartwig et al. “Machine Learning Detects First Star Diversity in Stellar Archaeological Data” of[{” attribute=””>Astrophysical Journal on March 22, 2023.

For more on this research, see Artificial Intelligence Sheds New Light on the Mysterious First Stars.

Reference: “Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data” by Tilman Hartwig, Miho N. Ishigaki, Chiaki Kobayashi, Nozomu Tominaga and Ken’ichi Nomoto, 22 March 2023, The Astrophysical Journal.
DOI: 10.3847/1538-4357/acbcc6

Funding: Ministry of Education, Culture, Sports, Science and Technology-Japan, Japan Society for the Promotion of Science, UK Science and Technology Facility Council, Leverhulme Trust

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