OmniCosmos: Transferring Particle Physics Knowledge Across the Cosmos

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.