What could information about one galaxy say about the universe in which it resided? New research may hold the answer.
Imagine if you could look at a snowflake at the South Pole and determine the size and the climate of all of Antarctica. Or study a randomly selected tree in the Amazon rain forest and, from that one tree—be it rare or common, narrow or wide, young or old—deduce characteristics of the forest as a whole.
data, then sent them to one of the university’s computing clusters, a collection of computers with far more power than his MacBook Air. That computing cluster contained thedata. Ding’s model trained itself by taking a set of simulated universes and looking at the galaxies within them. Once trained, the model would then be shown a sample galaxy and asked to predict features of the universe from which it was sampled.
Machine learning has catapulted astrophysics research forward, too. Villaescusa-Navarro said, “As a community, we have been dealing with super-hard problems for many, many years. Problems that the smartest people in the field have been working on for decades. And from one day to the next, these problems are getting solved with machine learning.” Even generating a single simulated universe used to take a very long time.
Villaescusa-Navarro began to do his own calculations. His doubt focussed foremost on the way that the machine learning itself worked. “One thing about neural networks is that they are amazing at finding correlations, but they also can pick up on numerical artifacts,” he said.
In December, Genel, an expert on galaxy formation, presented the preliminary results of the paper to the galaxy-formation group he belongs to at the Center for Computational Astrophysics, in New York. “This was really one of the most fun things that happened to me,” he said. He told me that any galaxy-formation expert could have no other first reaction than to think, This is impossible.
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