EveNet: A Foundation Model for Particle Collision Data Analysis
This provides a unified and resource-efficient framework to accelerate discovery in particle physics, though it is incremental as it builds on existing deep learning methods in the domain.
The paper tackles computational challenges in deep learning for high-energy physics by introducing EveNet, a foundation model pretrained on 500 million simulated collision events, which outperforms state-of-the-art baselines in tasks like heavy resonance searches and shows transferability to experimental data by rediscovering the Υ meson.
While deep learning is transforming data analysis in high-energy physics, computational challenges limit its potential. We address these challenges in the context of collider physics by introducing EveNet, an event-level foundation model pretrained on 500 million simulated collision events using a hybrid objective of self-supervised learning and physics-informed supervision. By leveraging a shared particle-cloud representation, EveNet outperforms state-of-the-art baselines across diverse tasks, including searches for heavy resonances and exotic Higgs decays, and demonstrates exceptional data efficiency in low-statistics regimes. Crucially, we validate the transferability of the model to experimental data by rediscovering the $Υ$ meson in CMS Open Data and show its capacity for precision physics through the robust extraction of quantum correlation observables stable against systematic uncertainties. These results indicate that EveNet can successfully encode the fundamental physical structure of particle interactions, which offers a unified and resource-efficient framework to accelerate discovery at current and future colliders.