EveNet: A Foundation Model for Particle Collision Data Analysis

TL;DR

EveNet is a large-scale foundation model trained on simulated collider data that improves particle physics analysis, demonstrates transferability to real data, and enables precise physics measurements with high data efficiency.

Contribution

The paper introduces EveNet, a novel foundation model for collider physics that combines self-supervised and physics-informed training on 500 million simulated events, outperforming existing methods.

Findings

EveNet outperforms state-of-the-art baselines across multiple physics tasks.

EveNet successfully rediscovered the $d$ meson in CMS Open Data.

EveNet enables precise extraction of quantum correlation observables.

Abstract

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 $\Upsilon$ 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.