Sunday, July 7, 2019

Deep-CEE: The AI Deep Learning Tool Helping Astronomers Explore Deep Space

Image showing the galaxy cluster Abell 1689. Credit: NASA/ESA

Galaxy clusters are some of the most massive structures in the cosmos, but despite being millions of light years across, they can still be hard to spot. Researchers at Lancaster University have turned to artificial intelligence for assistance, developing “Deep-CEE” (Deep Learning for Galaxy Cluster Extraction and Evaluation), a novel deep learning technique to speed up the process of finding them. Matthew Chan, a PhD student at Lancaster University, presented this work at the Royal Astronomical Society’s National Astronomy meeting on July 4 at 3:45 pm in the Machine Learning in Astrophysics session.

Most galaxies in the universe live in low-density environments known as "the field", or in small groups, like the one that contains our Milky Way and Andromeda. Galaxy clusters are rarer, but they represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy.

During 1950s the pioneer of galaxy cluster-finding, astronomer George Abell, spent many years searching for galaxy clusters by eye, using a magnifying lens and photographic plates to locate them. Abell manually analysed around 2,000 photographic plates, looking for visual signatures the of galaxy clusters, and detailing the astronomical coordinates of the dense regions of galaxies. His work resulted in the ‘Abell catalog’ of galaxy clusters found in the northern hemisphere.

Deep-CEE builds on Abell’s approach for identifying galaxy clusters but replaces the astronomer with an AI model that has been trained to “look” at color images and identify galaxy clusters. It is a state-of-the-art model based on neural networks, which are designed to mimic the way a human brain learns to recognize objects by activating specific neurons when visualizing distinctive patterns and colors.

Chan trained the AI by repeatedly showing it examples of known, labelled, objects in images until the algorithm is able to learn to associate objects on its own. Then ran a pilot study to test the algorithm’s ability to identify and classify galaxy clusters in images that contain many other astronomical objects.

Source: ras.ac.uk

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