EveNet: A Foundation Model for Particle Collision Data Analysis

TL;DR

EveNet introduces an event-level foundation model for collider data by pretraining a shared particle-cloud representation with a hybrid self-supervised and physics-informed objective on ~500 million simulated events. Its PET backbone supports discriminative and generative tasks, enabling transfer to diverse downstream analyses, including heavy-resonance searches, exotic Higgs decays, quantum correlations in tt̄, and anomaly detection on CMS Open Data, all with strong data efficiency. Across four downstream benchmarks, EveNet-Full consistently outperforms or matches task-specific baselines, accelerates convergence, and demonstrates robustness to detector systematics, while enabling a single, scalable workflow for multiple analyses. The results support a paradigm shift toward unified, resource-efficient event-level representations in HEP, with potential for integration into differentiable simulations and autonomous data-analysis pipelines.

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 $Υ$ 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.

Figures (8)

• Figure 1: Overview of the EveNet model architecture and tasks.(a): Collision events are reconstructed as sets of physics objects and global observables, which form the input to a point–edge transformer encoder. Task-specific decoders extend this shared representation to both generative and discriminative objectives. (b): Self-supervised generation masks a subset of input objects, while supervised generation predicts invisible particles or full event completions using diffusion. (c): Discriminative decoders branch into three task heads aligned with common analysis goals: identifying the physics process (Cls), matching reconstructed objects to their parent resonances (Asn), and grouping objects into final-state categories for event interpretation (Seg).
• Figure 2: Sensitivity across the $(m_X,m_Y)$ grid for $X\!\to\! YH_\mathrm{SM}$ with $Y\!\to\! b\bar{b}$ and $H_\mathrm{SM}\!\to\! WW^*$. For the individual training configuration, the figure shows five panels: (i) the maximum SIC, evaluated with a minimum background yield of 10 events, as a function of $m_X$ (with $m_Y$ indicated by the top axis); (ii) a per-bin "winner" map indicating the method achieving the highest maximum SIC; (iii) the effective number of optimisation steps required to reach the minimum validation loss; (iv) the available signal statistics per mass point; and (v) the ratio of the maximum SIC of each baseline method to that of the pretrained EveNet--Full. For the parameterized training configuration across the full mass grid, only panels (i), (ii), and (v) are shown, since direct loss comparisons between training strategies are not meaningful and the signal statistics are identical to those in the individual-training case.
• Figure 3: Out-of-distribution generalisation to exotic Higgs-boson decays. (a) Maximum Significance-Improvement Characteristic and (b) pairing efficiency as a function of training sample size, expressed relative to a typical search for new physics dataset of 200 K events. Blue markers denote EveNet--Full, yellow indicates training from scratch, pink corresponds to SSL pretraining, and grey shows SPANet as the comparison benchmark. Circular, cross, square, and diamond markers represent 5%, 15%, 50%, and 150% of the typical dataset size, respectively; the vertical dashed line denotes the 100% reference scale. Solid lines indicate fine-tuning only the classification head, while dashed(dotted) lines include both classification and assignment(segmentation) heads. For both panels, scatter points show performance scaling with dataset size, and the inset bar plots provide zoomed comparisons at the largest training fraction (150%).
• Figure 4: Precision measurement of quantum correlations in top-quark pairs. (a) Precision on the entanglement-sensitive observable $D$. (b) lepton–quark pairing accuracy as a function of training sample size, shown as a fraction of a typical dileptonic $t\bar{t}$ analysis dataset, and Blue markers denote EveNet--Full, yellow markers indicate training from scratch, and pink markers correspond to the SSL-pretrained variant. Circular markers represent training with 1.5% of typical dataset statistics, square markers correspond to 15%, and diamond markers denote 150%; the vertical dashed line marks the 100% scale, corresponding to 1 M events commonly available in LHC precision measurements. For both panels, the main scatter plot shows performance evolution with training size, while the inset bar plots present zoomed-in comparisons for the largest training point (150%) to highlight the residual differences in model performance.
• Figure 5: Collision-data anomaly detection using dimuon point-cloud generation. (a) Median $\ell$-reweighted anomaly significance for four train$\rightarrow$test configurations: OS$\!\rightarrow$OS, SS$\!\rightarrow$OS, OS$\!\rightarrow$SS, and SS$\!\rightarrow$SS. Blue markers denote EveNet--Full, yellow denote models trained from scratch, pink correspond to EveNet--SSL. The horizontal dashed line marks the published reference significance. (b) Calibration magnitude required to restore exact dimuon topology, shown as the relative per-event momentum adjustment (%) in the OS region. Uncertainty bands in both panels represent the spread across eight independently trained models and statistical variation from 200.0 resamplings per model.