To learn more about the deepest reaches of our own galaxy and the mysteries of star formation, Japanese researchers have created a deep learning model. The Osaka Metropolitan University-led team used artificial intelligence to pore through the vast amounts of data being acquired from space telescopes, finding bubble-like structures that had not been included in existing astronomical databases.

The findings have been published in Publications of the Astronomical Society of Japan.

The Milky Way galaxy we live in, like other galaxies in the universe, has bubble-like structures formed mainly during the birth and activity of high-mass stars. These so-called Spitzer bubbles hold important clues to understanding the process of star formation and galaxy evolution.

Graduate School of Science student Shimpei Nishimoto and Professor Toshikazu Onishi collaborated with scientists from across Japan to develop the deep learning model. Using data from the Spitzer Space Telescope and James Webb Space Telescope, the model employs AI image recognition to efficiently and accurately detect Spitzer bubbles. They also detected shell-like structures that are thought to have been formed by supernova explosions.

"Our results show it is possible to conduct detailed investigations not only of star formation, but also of the effects of explosive events within galaxies," stated graduate student Nishimoto.

Professor Onishi added, "In the future, we hope that advancements in AI technology will accelerate the elucidation of the mechanisms of galaxy evolution and star formation."