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Researchers have developed an Artificial Intelligence-powered tool that has been trained to “look” at color images and identify galaxy clusters quickly.


Deep Learning for Galaxy Cluster Extraction and Evaluation.

Deep-CEE builds on Abell’s approach for identifying galaxy clusters.

It replaces the astronomer with an Artificial Intelligence model.

Deep-CEE model is based on neural networks.

These neural networks are designed to mimic the way a human brain learns to recognize objects by activating specific neurons when visualizing distinctive patterns and colors.

Artificial Intelligence models are repeatedly trained until the algorithm is able to learn to associate objects on its own.


Galaxy clusters represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy.


Deep-CEE has successfully applied to the Sloan Digital Sky Survey.

This model will run on revolutionary surveys such as the Large Synoptic Survey Telescope (LSST).

New telescopes (LSST) have enabled astronomers to observe wider and deeper for studying the large-scale structure of the universe and mapping its vast undiscovered content.

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