Artificial intelligence finds the first stars were not alone

March 23, 2023
Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU)
National Observatory of Japan (NAOJ)

Figure 1. 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 stars, 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. (Credit: Kavli IPMU)

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.

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.

fig 3
Figure 2. Carbon vs. iron abundance of extremely metal-poor (EMP) stars. The colour bar shows the probability for mono-enrichment from our machine learning algorithm. Stars above the dashed lines (at [C/Fe] = 0.7) are called carbon-enhanced metal-poor (CEMP) stars and most of them are mono-enriched. (Credit: Hartwig et al.)

Hartwig has made the code developed in this study publicly available at

Details of their study were published on March 22 in The Astrophysical Journal.

Figure 3. (from left) Visiting Senior Scientist Ken’ichi Nomoto, Visiting Associate Scientist Miho Ishigaki, Kavli IPMU Visiting Associate Scientist Tilman Hartwig, Visiting Senior Scientist Chiaki Kobayashi, and Visiting Senior Scientist Nozomu Tominaga. (Credit: Kavli IPMU, Nozomu Tominaga)

Paper details
Journal: The Astrophysical Journal
Paper title: Machine learning detects multiplicity of the first stars in stellar archaeology data
Authors: Tilman Hartwig (1, 2, 3), Miho N. Ishigaki (4, 5, 3), Chiaki Kobayashi (6, 3), Nozomu Tominaga (4, 7, 3) and Ken'ichi Nomoto (3)

Author affiliations:
1 Institute for Physics of Intelligence, School of Science, The University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
2 Department of Physics, School of Science, The University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
3 Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan
4 National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588, Japan
5 Astronomical Institute, Tohoku University, 6-3, Aramaki, Aoba-ku, Sendai, Miyagi 980-8578, Japan
6 Centre for Astrophysics Research, Department of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield AL10 9AB, UK
7 Department of Physics, Faculty of Science and Engineering, Konan University, 8-9-1 Okamoto, Kobe, Hyogo 658-8501, Japan

DOI: 10.3847/1538-4357/acbcc6 (published March 22, 2023)
Paper abstract (The Astrophysical Journal) 
Pre-print (

Research contact
Chiaki Kobayashi
Visiting Senior Scientist
Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU)
The University of Tokyo
* Please change _at_ to @

Media contact
Motoko Kakubayashi
Press officer
Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU)
The University of Tokyo
TEL: 04-7136-5980
* Please change _at_ to @

Related links
Birth of elements (Leverhulme Trust)
AI Finds that First Stars were Clustered Together (NAOJ)
AI Finds that First Stars were Clustered Together (Subaru Telescope)