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AI for probing Dark Matter: Hierarchical World Models for Cosmology, RL Agents for Experimental Signatures.
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Ibrahim Elsharkawy is a PhD student at the University of Toronto and the Vector Institute, and a researcher affiliated with Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC). His research interests surround generative models for physical systems. How can such models be used as unique tools for physics discovery? Does learning a distribution of physical data imply learning the underlying physical laws? Can such models be constrained and interpretable by construction? Can methods developed for interpretability for these physical models be applied to generative models?
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There are two routes to probe DM. The first involves purpose-built experiments that assume dark matter does indeed interact non-gravitationally with the non-dark universe. The second treats gravity as the only probe. In this talk, we present two projects aligned with each perspective. In the first, we build hAIthem, a framework composed of a non-literature-biased RL agent that, when coupled to LLM swarms, is capable of proposing novel DM experimental signatures. In the second, we introduce Astrolabe, a hierarchical world model for cosmology, capable of cheaply simulating the structure of the universe across a large dynamic range of scales for many dark matter models, without the need for fine-tuning.
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- Workflow status: Published
- Created by: tnevels9
- Created: 10/27/2026
- Modified By: tnevels9
- Modified: 10/27/2026
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