Scientists say AI can find new Physics faster but not without risks
Researchers from the Flatiron Institute and Princeton University demonstrated that AI using transfer learning could accelerate the search for new physics beyond the standard cosmological model (ΛCDM) by reducing costly simulations by over tenfold. However, the study warns of risks like 'negative transfer,' driven by physical model degeneracies, which could distort findings if not properly addressed.
A team of scientists has shown that artificial intelligence (AI) could significantly speed up the discovery of new physics in cosmology by leveraging transfer learning. Published in the *Journal of Cosmology and Astroparticle Physics*, the research highlights how AI can efficiently explore theories beyond the standard cosmological model (ΛCDM), which currently explains large-scale universe features but is considered incomplete. The method involves pre-training AI on simpler, less expensive ΛCDM simulations before applying it to more complex models. Adrian Bayer, a cosmologist at the Flatiron Institute and Princeton University, explained this approach as a shortcut, reducing the need for high computational power. Co-author Veena Krishnaraj noted that this strategy could cut the number of expensive simulations required by over ten times, making research more accessible. However, the study identifies risks associated with 'negative transfer,' where AI may incorrectly learn patterns due to physical degeneracies in models. Krishnaraj emphasized the need to mitigate these errors to ensure accurate results. The researchers tested this technique using simulations, and if successful at scale, it could revolutionize future cosmological research by lowering costs and accelerating discoveries. The findings suggest AI could play a pivotal role in investigating phenomena like modified gravity, massive neutrinos, and dark energy particles. By streamlining simulations, the method could help scientists explore new physics more efficiently while addressing potential biases in AI training.
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