Astronomers use the term “early Universe” to refer to the period of time shortly after the Big Bang, which is believed to have occurred around 13.8 billion years ago. During this period, the Universe was very different from what it is today. It was hot, dense, and rapidly expanding, and the fundamental forces of nature were still unifying into the single force we know today.
In the early Universe, the first stars and galaxies began to form, and the seeds of the large-scale structure of the Universe were sown. By studying the early Universe, astronomers hope to gain insights into the formation and evolution of galaxies, the nature of dark matter and dark energy, and the fundamental physics that govern the behavior of the Universe. This can be done through observations of the cosmic microwave background radiation, the study of the chemical and elemental composition of the first stars and galaxies, and through theoretical models and simulations of the early Universe.
Surprisingly or not, AI can help astronomers dive even deeper into the mysteries of the early Universe.
Supernovae joined stars in the Early Universe
Researchers from the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) have used machine learning and advanced supernova nucleosynthesis to study the early Universe. Their study, published in The Astrophysical Journal, found that the majority of observed second-generation stars existing in the Universe got enriched by multiple supernovae, as EurekAlert reveals.
These metal-poor stars were analyzed using a supervised machine-learning algorithm trained on theoretical supernova nucleosynthesis models.
The team’s findings provide the first quantitative constraint based on observations of the multiplicity of the primordial stars, and the algorithm developed in this study could help make the most of diverse chemical fingerprints in stars that weren’t rich in metals and that were discovered by the Prime Focus Spectrograph.