OmniCosmos: Transferring Particle Physics Knowledge Across the Cosmos
Vinicius Mikuni, Ibrahim Elsharkawy, and Benjamin Nachman

TL;DR
This paper demonstrates that foundation models trained on collider physics data can be effectively transferred to cosmology, improving predictions of cosmological parameters and galaxy velocities, thus showing cross-disciplinary generalization.
Contribution
It introduces the first successful transfer of a collider physics foundation model to cosmology, broadening the scope of foundation models across scientific fields.
Findings
Improved prediction of cosmological parameters
Enhanced halo and galaxy velocity predictions
First cross-field application of collider physics models
Abstract
Foundation models build an effective representations of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearned foundation model for collider physics and showed that it could significantly advance discovery potential across collider experiments. In this paper we go beyond collider physics and show that Foundation Models trained on collider data can help improve the prediction of cosmological parameters and to predict halo and galaxy velocities in different datasets from CosmoBench. This is the first time a collider physics model is shown to generalize across scientific fields.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Astrophysics and Cosmic Phenomena
