Artificial intelligence is already playing a major role in helping cosmologists study the universe. Now, new research suggests a machine learning technique called transfer learning could make the search for new physics much faster and less expensive. However, the study also uncovered a surprising downside: AI can sometimes become so dependent on what it has already learned that it struggles to recognize something truly new.
The study, published in the Journal of Cosmology and Astroparticle Physics (JCAP), examined how transfer learning might help researchers investigate theories that go beyond the standard cosmological model.
The current standard model of cosmology, known as ΛCDM, successfully explains many large-scale features of the universe, including its expansion and the distribution of galaxies. Yet scientists believe the model is not the final answer.
Recent observations have raised questions that could point toward new physics, including the effects of massive neutrinos, modified gravity, and evolving dark energy. Exploring these possibilities requires researchers to generate enormous numbers of detailed computer simulations, each representing a virtual universe built using different physical assumptions.
Producing these simulations is computationally expensive and often demands substantial computing power.
The researchers investigated whether transfer learning could make this process more efficient.
Transfer learning allows an AI system to apply knowledge gained from one task to another related task. Instead of training a neural network entirely on the most complex and computationally costly simulations, the team first trained it on simpler simulations based on ΛCDM. This initial phase, known as pretraining, was then followed by additional training using more sophisticated models that include potential new physics.
"It's basically a shortcut," explains Adrian Bayer a cosmologist at the Flatiron Institute and Princeton University, co-author of the study. "Usually people train the AI directly on the most computationally expensive simulations. What we do instead is first use simpler and less expensive ΛCDM simulations to give the AI an idea of what's happening, and only afterward move to the more complex models."
Bayer compares the approach to learning from textbooks.
"You first read a basic book to get an idea of the knowledge," says Bayer, "and then move to the really complicated book."
According to first author Veena Krishnaraj, an undergraduate student at Princeton University, this strategy prevents the AI from having to "digest everything at once."
The results were striking. In some cases, transfer learning reduced the number of expensive simulations required by more than a factor of ten.
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