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Studying galaxies is essential for astronomers seeking to gain an understanding of the universe’s history and future.

By analyzing the light emitted from galaxies, they can gather information about celestial bodies that inhabit them, including what they are made of, their temperatures and age.

Existing processes for this type of astronomical observation use ground or satellite-based surveying. It can be time-consuming and expensive.

That’s why Youssef Zaazou, a graduate student in the Faculty of Science at ���˵�Ӱ University, has developed an innovative AI-based, image-processing technique to allow astronomers to significantly and quickly improve upon the capacity of existing observations.

This use of AI could transform astronomical research.

His project, titled , represents an industry first and has received enthusiastic praise from his supervisors and astronomers.

“This project bridges my love for astronomy and my interest in machine learning,” Mr. Zaazou said. “I wanted to be an astronomer, but I realized it was somewhat impractical. This project is a good concession.”

 were recently published in the prestigious .

Exploring the cosmos with AI

Mr. Zaazou’s work focuses on using generative AI to map galaxy images across photometric bands: different wavelengths of light such as ultraviolet, visible, and infrared.

The novel approach will enable astronomers to make high-quality predictions on unseen regions of space, which may assist them in allocating telescope resources more efficiently.

“No one has attempted to transform galaxy photos across photometric bands using AI before,” Mr. Zaazou explained. “Generative AI remains a relatively underutilized tool in astronomy. We hope that this work will motivate the astronomical community to incorporate AI in their toolbox.”

The technique allows astronomers to fill gaps in existing datasets, highlights the untapped potential of AI in astronomy and demonstrates how the tools can automate tasks like image translation.

“Astronomers rely on observations across wavelengths because each wavelength offers unique insights into a galaxy’s properties,” Mr. Zaazou explained. “However, collecting this data is resource-intensive. Our AI models provide an efficient way to do the work.”

While the underlying AI techniques are established, Mr. Zaazou emphasizes that their application to this specific astronomical challenge is both novel and impactful.

“The findings are significant and could lead to productive results, and that isn’t always the case for a master of science or even PhD thesis. I feel very fortunate to have had this opportunity.”

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Youssef Zaazou is available for an interview. For more information or to arrange interviews, please contact Nicole Squires, communications advisor, Faculty of Science, ���˵�Ӱ University, at n.squires@mun.ca or (709) 864-2019.