The first stars began the crucial transition from a primordial universe to one enriched with heavier elements. These stars formed in pristine minihalos around redshift 6-30.

According to astronomers, these stars were metals that do not contain heavy elements. The next generation of stars contained only a small amount of the heavy elements produced by the first stars. To understand the universe in its infancy, researchers must study these metal-poor stars.

In a new study, a team of scientists used machine learning and state-of-the-art supernova nucleosynthesis to observe these first stars. They found that most observed second-generation stars in the universe were enriched with multiple supernovae.

Using AI, the team analyzed elemental amounts in more than 450 very metal-poor stars observed to date. They found that 68% of the observed very metal-poor stars had a chemical fingerprint consistent with enrichment from several previous supernovae based on the newly built supervised machine learning technique trained on theoretical supernova nucleosynthesis models.

The team’s results provide the first quantitative constraint based on observations of the multitude of the first stars.

Lead author Hartwig said: “The multitude of the first stars has so far only been predicted on the basis of numerical simulations, and until now there has been no way to observably examine the theoretical prediction. Our result suggests that most early stars formed in small clusters, so that several of their supernovae may contribute to the metal enrichment of the early interstellar medium.”

“Our new algorithm provides an excellent tool to interpret the big data we will have over the next decade from ongoing and future astronomical surveys around the world..

Visiting Associate Scientist and National Astronomical Observatory of Japan, Assistant Professor Miho Ishigaki said: “Currently, the available data from ancient stars is the tip of the iceberg within the solar environment. The Prime Focus Spectrograph, an advanced multi-object spectrograph on the Subaru telescope, developed by the international collaboration led by Kavli IPMU, is the best tool to discover ancient stars in the outer regions of the Milky Way, far beyond the solar environment.”

Visiting Senior Scientist and the University of Hertfordshire, Professor Chiaki Kobayashi said: “The new algorithm invented in this study opens the door to making 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 cloud of gas with a mass one million times greater than that of the sun.”

“However, our new finding strongly suggests that the first stars were not just born, but instead formed as part of a star cluster or a binary or multiple star system. This also means that we can expect to see gravitational waves from the first binaries shortly after the Big Bang.” , which can be detected in future missions in space or on the moon.”

Magazine reference:

  1. Tilman Hartwig et al. Machine Learning detects multitude of first stars in stellar archeology data The Astrophysical Journal. DOI 10.3847/1538-4357/acbcc6