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Turkish Scientists Discover 4 Dwarf Galaxy Remnants Swallowed by the Milky Way
Machine learning and data from the Gaia satellite and SDSS-V telescopes revealed new structures named FO1-FO5 in the study. The findings will contribute to understanding galaxy formation processes.
Scientists from the Department of Astronomy and Space Sciences at Istanbul University's Institute of Science discovered the remnants of four new dwarf galaxies and one disrupted globular cluster swallowed by the Milky Way in the past.
The study, led by Dr. Olcay Plevne and PhD student Furkan Akbaba, was supported by the Istanbul University Scientific Research Projects Coordination Unit's doctoral thesis program and within the scope of the TÜBİTAK 1001 project. The researchers combined high-resolution photometric, astrometric, and spectral data obtained from the European Space Agency's (ESA) Gaia satellite and ground-based telescopes under the SDSS-V project.
Five New Structures: FO1 to FO5
The newly discovered structures were named FO1, FO2, FO3, FO4, and FO5, drawing from the first letters of the authors' surnames. Four of them were classified as dwarf galaxy remnants, and one as a disrupted globular cluster, identified thanks to differences in chemical composition. While more than 15 similar structures had previously been described in the scientific literature, this discovery brings the total number to over 19.
The study was published in Publications of the Astronomical Society of the Pacific (PASP), a prestigious Q1 journal in astronomy. The journal's impact factor was recorded as 6.8.
Machine Learning and Chemodynamic Method
In a statement to Anadolu Agency, Dr. Plevne said the main goal of the research was to trace the remnants of satellite dwarf galaxies that the Milky Way incorporated through mergers in the past. Comparing the method to 'a type of DNA analysis,' Plevne said, 'We uncovered four new structures from among previously studied and well-known ones using new techniques such as machine learning and big data analysis.'
We identified the stars in these structures. Their numbers are currently low and they are quite scattered. Wherever you look in the galaxy, you can see them. However, through a deep analysis we were able to figure out which ones are connected to each other and where they came from. In the future, we will seek answers to questions such as when they collided, how they got there, and what these dwarf galaxies were really like.
PhD student Furkan Akbaba stated that they worked on a huge dataset covering approximately 1.4 million stars by combining data from the Gaia satellite and two separate SDSS-V telescopes located in the northern and southern hemispheres.
'We Didn't Tell the AI, Find This'
Akbaba emphasized that the most critical stage of the work was the machine learning model they used. He explained that the model, called 'chemodynamic,' which combines chemical composition and dynamic analysis data, successfully identified the known 15 structures first, without any guidance, and then found four additional structures that did not match the literature.
We actually worked with two types of data. On one hand, we examined the chemical composition of stars, and on the other, we performed dynamic analyses. We fed both pieces of information into the machine learning model called 'chemodynamic.' We didn't tell the AI, 'find this, find that.' It first discovered 15 structures on its own, and when it then found four more structures not in the literature, we realized it was a discovery.
The research team stated that as new observational data arrives, the age of these structures, the time of merger with the galaxy, and their masses can be determined, and this information will provide valuable observational data to be used in galaxy formation simulations. The findings are expected to shed light on the evolution of the Milky Way and galaxy formation processes in general.
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