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Scientists use machine learning to classify millions of new galaxies
Classifying galaxies by hand and eye was already hard over 20 million of them would be impossible. So machine learning came in handy.
Despite being a relatively new field, image-based machine learning can already accomplish impressive things, but how does imaging analysis could be applied to astronomy? A recent study used machine learning to describe the morphological characteristics of galaxies. The team involved used nearly 27 million galaxies from the Dark Energy Survey (DES) to train, test, and finally implement their method.
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What the past has taught us
If we can know the age of each galaxy we observe and know its position, we can better understand how it got there. But cataloging tens and hundreds of millions of galaxies by hand just won’t cut it.
Planetarium is shaping the world of astrophysics for the next decade newsletter.co.uk - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from newsletter.co.uk Daily Mail and Mail on Sunday newspapers.
AI algorithms in large-scale astronomy are mandatory, study says
Katyanna Quach Wed 14 Apr 2021 // 07:27 UTC Share
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A neural network has helped astronomers catalog a whopping 27 million galaxies collected from one of the largest astronomical surveys probing the mysterious nature of dark energy.
The Dark Energy Survey (DES) kicked off in 2013, and aims to snap galaxies and supernovae across an eighth of the Earth s night sky. Although that may not sound like much, the number of objects filling such a region of space – giving us a window onto the universe as far back as when it was just six billion years old, less than half of its age today – is staggering.
27 million galaxy morphologies quantified and cataloged with the help of machine learning upenn.edu - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from upenn.edu Daily Mail and Mail on Sunday newspapers.