Introduction: The Challenge of Testing New Physics
Artificial intelligence is increasingly used in cosmology to analyze vast datasets and simulate the universe. However, testing theories beyond the standard cosmological model, known as ΛCDM, remains computationally demanding. While ΛCDM successfully describes many properties of the universe—from its expansion to the distribution of galaxies—physicists know it is probably incomplete. Recent observations hint that phenomena such as massive neutrinos, modified gravity, or evolving dark energy could point toward new physics beyond the current model. Testing these alternatives requires running huge numbers of high-precision simulations of virtual universes under different physical assumptions, often demanding enormous computational resources.
Transfer Learning: A Shortcut for Cosmology
A study published in the Journal of Cosmology and Astroparticle Physics explores how a machine-learning strategy known as transfer learning could dramatically reduce the computational cost of searching for new physics. Transfer learning is a technique in which AI systems reuse knowledge acquired from one task to accelerate learning in another. In this case, researchers first trained a neural network on simulations based on ΛCDM—this is known as pretraining—and then adapted it to more complex cosmological models that include possible new physics.
"It's basically a shortcut," explains Adrian Bayer, a cosmologist at the Flatiron Institute and Princeton University and co-author of the study. "Usually, people train the AI directly on the most computationally expensive simulations. With transfer learning, we can start from a model that already understands the basic structure of the universe and then fine-tune it for new scenarios."
The Risk of Over-Reliance on Existing Knowledge
While transfer learning offers efficiency, the study also reveals an unexpected risk: sometimes AI systems can become too reliant on what they already know. The pretrained model, having learned the patterns of ΛCDM, may struggle to adapt to fundamentally different physics. In some cases, the AI failed to recognize subtle deviations from the standard model because it was biased by its prior training.
"The AI can become overconfident in the standard model and miss signs of new physics," says Bayer. "It's like a scientist who is so used to one theory that they overlook evidence that contradicts it. In a way, the AI needs to 'unlearn' some of its old knowledge to truly discover something new."
Methodology: Simulations and Neural Networks
The research team used the Quijote simulations, a suite of high-resolution cosmological simulations that model the universe under different physical assumptions. They trained a neural network on ΛCDM simulations and then fine-tuned it on simulations that included massive neutrinos and modified gravity. The differences between these models are subtle, but they reveal how changes in underlying physics can affect the formation and distribution of cosmic structures.
The study found that transfer learning reduced the computational cost by up to 90% compared to training from scratch. However, the accuracy of the fine-tuned model depended on how similar the new physics was to ΛCDM. For models that were very different, the AI's performance degraded, indicating that the pretrained knowledge was not always beneficial.
Implications for Future Research
This research highlights both the promise and pitfalls of using AI in cosmology. Transfer learning could enable scientists to explore a wider range of theoretical models without requiring exorbitant computing power. However, it also underscores the need for careful validation to ensure that AI systems do not inadvertently reinforce existing biases.
"We need to develop methods that allow AI to adapt more flexibly," says Bayer. "One approach is to use techniques that explicitly encourage the model to forget irrelevant information while retaining useful patterns. This could help the AI remain open to truly novel discoveries."
The study was conducted by researchers at SISSA Medialab, the Flatiron Institute, and Princeton University, and has been reviewed according to Science X's editorial process and policies. It is based on a preprint source that has been trusted and proofread.
Conclusion: A Balancing Act
As AI becomes a more integral tool in scientific discovery, understanding its limitations is crucial. The ability to 'unlearn' may be as important as the ability to learn. By balancing efficiency with flexibility, researchers can harness AI's power to push the boundaries of cosmology and potentially uncover new laws of physics.
This article is based on reporting by Phys.org. Read the original article.
Originally published on phys.org
