ABOVE AVERAGE IS OVER:

In a paper published in December in Astronomy & Astrophysics, Schawinski and his ETH Zurich colleagues Dennis Turp and Ce Zhang used generative modeling to investigate the physical changes that galaxies undergo as they evolve. (The software they used treats the latent space somewhat differently from the way a generative adversarial network treats it, so it is not technically a GAN, though similar.) Their model created artificial data sets as a way of testing hypotheses about physical processes. They asked, for instance, how the "quenching" of star formation--a sharp reduction in formation rates--is related to the increasing density of a galaxy's environment.

For Schawinski, the key question is how much information about stellar and galactic processes could be teased out of the data alone. "Let's erase everything we know about astrophysics," he said. "To what degree could we rediscover that knowledge, just using the data itself?"

First, the galaxy images were reduced to their latent space; then, Schawinski could tweak one element of that space in a way that corresponded to a particular change in the galaxy's environment--the density of its surroundings, for example. Then he could re-generate the galaxy and see what differences turned up. "So now I have a hypothesis-generation machine," he explained. "I can take a whole bunch of galaxies that are originally in a low-density environment and make them look like they're in a high-density environment, by this process." Schawinski, Turp and Zhang saw that, as galaxies go from low- to high-density environments, they become redder in color, and their stars become more centrally concentrated. This matches existing observations about galaxies, Schawinski said. The question is why this is so.

The next step, Schawinski says, has not yet been automated: "I have to come in as a human, and say, 'OK, what kind of physics could explain this effect?'" For the process in question, there are two plausible explanations: Perhaps galaxies become redder in high-density environments because they contain more dust, or perhaps they become redder because of a decline in star formation (in other words, their stars tend to be older). With a generative model, both ideas can be put to the test: Elements in the latent space related to dustiness and star formation rates are changed to see how this affects galaxies' color. "And the answer is clear," Schawinski said. Redder galaxies are "where the star formation had dropped, not the ones where the dust changed. So we should favor that explanation."

The approach is related to traditional simulation, but with critical differences. A simulation is "essentially assumption-driven," Schawinski said. "The approach is to say, 'I think I know what the underlying physical laws are that give rise to everything that I see in the system.' So I have a recipe for star formation, I have a recipe for how dark matter behaves, and so on. I put all of my hypotheses in there, and I let the simulation run. And then I ask: Does that look like reality?" What he's done with generative modeling, he said, is "in some sense, exactly the opposite of a simulation. We don't know anything; we don't want to assume anything. We want the data itself to tell us what might be going on."

The apparent success of generative modeling in a study like this obviously doesn't mean that astronomers and graduate students have been made redundant--but it appears to represent a shift in the degree to which learning about astrophysical objects and processes can be achieved by an artificial system that has little more at its electronic fingertips than a vast pool of data. "It's not fully automated science--but it demonstrates that we're capable of at least in part building the tools that make the process of science automatic," Schawinski said.

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