AI has the potential to accelerate the search for new physics, but it can also be so knowledgeable that we can’t see what’s right in front of us.
Artificial intelligence could make the search for new laws of physics much cheaper and faster, according to new research published in . Journal of Cosmology and Astroparticle Physics (JCAP). But the study also points to some unexpected downsides. In some situations, AI can become too dependent on previous training and struggle to recognize truly new phenomena.
AI has become an important tool in cosmology, helping researchers analyze vast amounts of data about the universe. However, exploring ideas beyond the current standard cosmological model, known as ΛCDM, remains a very costly computational challenge.
Although ΛCDM has been successful in explaining many observed features of the universe, such as the expansion and large-scale distribution of galaxies, scientists do not believe that ΛCDM tells the whole story. Recent observations suggest that phenomena such as giant neutrinos, modified gravity, and evolving dark energy may reveal physics beyond current models.
Exploring these possibilities requires researchers to generate vast numbers of detailed simulations of virtual universes based on different physical assumptions. Creating these simulations often requires significant computational power and time.
Transfer learning offers a faster route
The researchers machine learning An approach called transfer learning may help alleviate that burden.
Transfer learning allows AI systems to apply knowledge from one task to learn another task more efficiently. Rather than starting from scratch, AI builds on what it has already learned.
For this study, the team first trained a neural network using simulations based on ΛCDM. This initial training process, known as pre-training, gave the AI a foundation before it was exposed to more complex cosmological models, including potentially new physics.
“It’s basically a shortcut,” explains cosmologist Adrian Beyer of the Flatiron Institute. princeton universityco-author of the study. “Typically, people train AI directly on the most computationally expensive simulations. What we do instead is first use simple, low-cost ΛCDM simulations to make the AI aware of what’s going on, and then move on to more complex models.”
Beyer likens the process to learning from a textbook. “We start by reading basic books to understand the knowledge, and then we move on to very complex books,” Beyer says.
This approach eliminates the need for AI to “digest everything at once,” said Veena Krishnaraj, an undergraduate at Princeton University and lead author of the paper.
This strategy has proven to be highly effective. In some cases, transfer learning has reduced the number of costly simulations required by more than a factor of 10.
When prior knowledge matters
The study also uncovered a less obvious challenge known as negative transfer.
Using Beyer’s textbook analogy, imagine a medical student learning from introductory material and then encountering a rare disease that resembles a common disease. Existing knowledge is usually helpful, but in some cases it can lead to incorrect conclusions.
Similar problems can occur with AI systems. The particular signals generated by the new physics could be very similar to patterns that the AI has already learned from standard cosmological models. The AI may then interpret new information through the lens of its previous training, making it more difficult to recognize anything that is truly different.
Researchers confirmed this effect while studying simulations involving giant neutrinos. Some of the observable consequences of neutrino mass are very similar to changes associated with an existing ΛCDM parameter called σ8, which measures how strong clusters of matter are throughout the universe.
The two effects appear so similar that pretrained neural networks initially had difficulty distinguishing between them.
“Negative transitions are not random; they are caused by the underlying physical degeneracy of the model,” says Krishnaraj. In other words, different physical parameters can create nearly identical observable signatures, making it difficult for AI to correctly separate them. “So this is something that we need to be aware of and try to mitigate,” she concluded.
Expectations and risks for future cosmology
The findings demonstrate both the benefits and potential pitfalls of applying fundamental model strategies to physics. These approaches are conceptually similar to techniques used in modern generative AI systems and large-scale language models.
As the authors note in their paper, pre-training can speed up inference, “but it can also impede learning new physics.”
So far, this method has only been tested using simulation. But researchers believe this provides an important foundation for future applications involving real-world astronomical observations.
Its value is likely to increase as the next generation of cosmological surveys begins to generate unprecedented amounts of high-precision data about the universe. When used judiciously, transfer learning can help scientists continue exploring physics beyond the Standard Model while analyzing that information much more efficiently.
The paper “Transfer learning beyond the standard model” by Veena Krishnaraj, Adrian E. Bayer, Christian Kragh Jespersen, and Peter Melchior has been published in JSTAT.
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