Benjamin Nachman

Measurements of longitudinal flow decorrelations in 13-TeV p p and 5.44-TeV Xe + Xe collisions with the ATLAS detector are presented. The measurements are performed using the two-particle correlation method, combining charged-particle tracks within | η | < 2.5 with either calorimeter energy clusters or towers within 4.0 < | η | < 4.9 . A template-based subtraction procedure is used to remove nonflow effects in both the p p and the Xe + Xe analyses. The dependence of the longitudinal flow decorrelations on the pseudorapidity separation between the particles is characterized via the slope parameter F n for the elliptic ( n = 2 ) and triangular ( n = 3 ) harmonic moments. The results are reported as a function of charged-particle multiplicity for the p p and Xe + Xe collision systems. Comparing the data to a color string-based model of the initial geometry indicates that in p p and peripheral Xe + Xe collisions, subnucleonic structure, and fluctuations in longitudinal energy deposition are needed to describe the data.

Data-intensive collider experiments increasingly require models that transfer across high-dimensional, partially observed measurements, yet most analyses still optimize separate models for each task. Here, to address this bottleneck, we introduce EVENET, an event-level foundation model pretrained on 500 million simulated collision events using self-supervised learning and physics-informed supervision. The model constructs a shared particle-cloud representation from the variable-length set of physics objects in each event. Across searches for heavy resonances and exotic Higgs decays, EVENET outperforms both established and state-of-the-art baselines, particularly in low-statistics regimes. We validate transfer to experimental data by rediscovering the ϒ meson in CMS Open Data. The pretrained model also supports precise extraction of quantum-correlation observables that remain stable under the tested systematic variations. Together, these results establish a reusable backbone for diverse collider analyses, improving downstream adaptation after a substantial one-time pretraining investment.

Foundation models build an effective representation of data that can be deployed on diverse downstream tasks. Previous research showed that the , a foundation model for collider physics, could significantly advance discovery potential across collider experiments. In this paper, we show that foundation models trained on collider data can transfer to cosmology. Specifically, we improve the prediction of cosmological parameters and halo and galaxy velocities in different datasets from . To the best of our knowledge, this is the first time a collider physics model has been shown to generalize across scientific fields.