AI analyses the stars

Artificial Intelligence (AI) has revolutionised our understanding of astronomy. It can enable researchers to analyse vast amounts of data in a matter of seconds, and this in turn can allow them to uncover new insights about the universe.

Nuclear astrophysics show that elements including and heavier than carbon in the universe are produced in stars. However, the first stars did not contain heavy elements, or ‘metals’, and the second generation of stars contained only small amounts.

Second generation stars occurred when the first stars exploded as supernovae. The resulting explosion of these first stars, which were composed predominantly of helium and hydrogen, produced a small amount of heavy elements in second generation stars, aka ‘metals’.

To better understand the physical properties of these first stars, from the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) and The University of Tokyo Institute for Physics of Intelligence took to observing the metal-poor second-generation stars in our Milky Way Galaxy.

AI analyses the elemental quantities in over 450 second-generation stars, and by applying a newly developed machine learning algorithm which is trained on theoretical supernova nucleosynthesis models, the teams found that 68% of metal-poor second-generation stars had a chemical fingerprint consistent with 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 and Assistant Professor at Kavli IPMU Tilman 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.”

Visiting Senior Scientist and University of Hertfordshire Professor, Chiaki Kobayashi, comments: “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.

“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.”

AI is rapidly transforming astronomy by enabling researchers to sift through vast amounts of data and uncover new and exciting insights about the universe. The discovery that the first stars were not alone is just one example of how AI is helping us better understand our place in the cosmos.