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AI Trained on Images from Cosmological Simulations Surprisingly Successful at Classifying Real Galaxies in Hubble Images (ucsc.edu) 20

A machine learning method which has been widely used in face recognition and other image- and speech-recognition applications, has shown promise in helping astronomers analyze images of galaxies and understand how they form and evolve. From a report: In a new study, accepted for publication in Astrophysical Journal and available online [PDF], researchers used computer simulations of galaxy formation to train a deep learning algorithm, which then proved surprisingly good at analyzing images of galaxies from the Hubble Space Telescope. The researchers used output from the simulations to generate mock images of simulated galaxies as they would look in observations by the Hubble Space Telescope. The mock images were used to train the deep learning system to recognize three key phases of galaxy evolution previously identified in the simulations. The researchers then gave the system a large set of actual Hubble images to classify.

The results showed a remarkable level of consistency in the neural network's classifications of simulated and real galaxies. "We were not expecting it to be all that successful. I'm amazed at how powerful this is," said coauthor Joel Primack, professor emeritus of physics and a member of the Santa Cruz Institute for Particle Physics (SCIPP) at UC Santa Cruz. "We know the simulations have limitations, so we don't want to make too strong a claim. But we don't think this is just a lucky fluke."

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AI Trained on Images from Cosmological Simulations Surprisingly Successful at Classifying Real Galaxies in Hubble Images

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  • Now what are astrophysics interns gonna put on their resume?

    • by Anonymous Coward

      "Worked with AI to more accurately identify galaxy formations from Hubble space data."

    • Re: (Score:2, Insightful)

      by Anonymous Coward

      Probably something a heck of a lot more valuable than analyzing images and avoiding bias using near impossible criteria.

      It isn't surprising a machine could beat a human at a totally mechanistic and inhuman task.

      Now those same students can better use their time to analyze large datasets produced by these algorithms and come up with results the algorithms never could... until we find a way to create such an algorithm. Which is something those students can also work on.

  • Still, they run a lot of other projects and some of those are almost as much fun.

  • This should be used even for the usual image recognition. You don't need 1000s of dog pictures/photos to detect a dog. A 3 year old child knows what a dog is - not by seeing 1000s but even one dog is enough. A generator AI using laws of physics/rotation/scaling etc should generate images for what a dog will look like using a base 3D model of a dog (like a dog toy you find in a toystore) [say from a given point of view/positioning of the 2D camera]. The final image recognizer will work even if the dog is m

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