Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning
This paper introduces a generalizable foundation model for calorimetry that leverages next-token transformer architectures combined with Mixture-of-Experts pre-training and parameter-efficient fine-tuning to enable modular, scalable, and computationally efficient simulation of particle showers across diverse materials and detector configurations without catastrophic forgetting.