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Galaxy clusters are some of the most massive structures in the cosmos, but despite being millions of lightyears 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 Ph.D. student at Lancaster University, is presenting this work at the Royal Astronomical Society’s National Astronomy meeting on 4 July at 3:45pm 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 and dark energy.

During 1950s the pioneer of galaxy -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 . His work resulted in the ‘Abell catalogue’ of galaxy clusters found in the .

Whether it was the Big Bang, Midas or God himself, we don’t really need to unlock the mystery of the origins of gold when we’ve already identified an asteroid worth $700 quintillion in precious heavy metals.

If anything launches this metals mining space race, it will be this asteroid—Psyche 16, taking up residence between Mars and Jupiter and carrying around enough heavy metals to net every single person on the planet close to a trillion dollars.

The massive quantities of gold, iron and nickel contained in this asteroid are mind-blowing. The discovery has been made. Now, it’s a question of proving it up.