In the era of growing exploitation of AI, astronomy can truly benefit from it too. A Brazilian astronomer proposes a multiple machine learning method (MMLM) that became an essential tool in finding and investigating star clusters.

Machine learning (ML) is a branch of artificial intelligence (AI) focused on building systems that autonomously learn from data. Therefore, ML systems use algorithms to identify patterns and make decisions based on experience.

In astronomy, ML can be implemented, for instance, to classify and analyze galaxies or stars. The algorithms can learn from sets of images classified by humans and once they are trained, they can categorize millions of new astronomical objects in seconds with higher consistency than a tired human eye.

Denilso Camargo, a Brazilian astronomer and discoverer of more than 1,000 star clusters, in a [research paper](https://arxiv.org/pdf/2506.13951) published in June 2025, proves that employing a combination of ML algorithms can be more effective when it comes to finding and analyzing stellar groupings.

“There are several machine learning available for astronomy exploration, and each approach has its own advantages and limitations, so I’m applying a multiple machine learning method for star cluster analysis. The combination of these algorithms leads to more robust results, since the limitations of one of them may be covered by the others,” Camargo told [Universelost.com](https://universelost.com/).

  
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AI in Astronomy? Machine Learning to Help Us Find and Explore Star Clusters