Rivka Galchen writes:

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. Or, what if, by looking at one galaxy among the hundred billion or so in the observable universe, one could say something substantial about the universe as a whole? A recent paper, whose lead authors include a cosmologist, a galaxy-formation expert, and an undergraduate named Jupiter (who did the initial work), suggests that this may be the case. The result at first seemed “crazy” to the paper’s authors. Now, having discussed their work with other astrophysicists and done various “sanity checks,” trying to find errors in their methods, the results are beginning to seem pretty clear. Francisco Villaescusa-Navarro, one of the lead authors of the work, said, “It does look like galaxies somehow retain a memory of the entire universe.”

The research began as a sort of homework exercise. Jupiter Ding, while a freshman at Princeton, wrote to the department of astrophysics, hoping to get involved in research. He mentioned that he had some experience with machine learning, a form of artificial intelligence that is adept at picking out patterns in very large data sets. Villaescusa-Navarro, an astrophysicist focussed on cosmology, had an idea for what the student might work on. Villaescusa-Navarro had long wanted to look into whether machine learning could be used to help find relationships between galaxies and the universe. “I was thinking, What if you could look at only a thousand galaxies and from that learn properties about the entire universe? I wondered, What is the smallest number we could look at? What if you looked at only one hundred? I thought, O.K., we’ll start with one galaxy.”

He had no expectation that one galaxy would provide much. But he thought that it would be a good way for Ding to practice using machine learning on a database known as CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations). Shy Genel, an astrophysicist focussed on galaxy formation, who is another lead author on the paper, explained CAMELS this way: “We start with a description of reality shortly after the Big Bang. At that point, the universe is mostly hydrogen gas, and some helium and dark matter. And then, using what we know of the laws of physics, our best guess, we then run the cosmic history for roughly fourteen billion years.” Cosmological simulations have been around for about forty years, but they are increasingly sophisticated—and fast. CAMELS contains some four thousand simulated universes. Working with simulated universes, as opposed to our own, lets researchers ask questions that the gaps in our observational data preclude us from answering. They also let researchers play with different parameters, like the proportions of dark matter and hydrogen gas, to test their impact. [Continue reading…]