Speaker
Description
Experiments at the Large Hadron Collider produce some of the most complex datasets in science. A central challenge in high-energy physics is to extract rare signals, reconstruct invisible particles such as neutrinos, and search for subtle deviations from the Standard Model with maximal sensitivity. These are fundamentally physics problems, but their scale and complexity call for new strategies.
In this talk, I will present EveNet, a large-scale foundation model developed for collider physics. Trained on billions of simulated events, it provides a common starting point for diverse analysis tasks, including improving search sensitivity, reconstructing hidden structures, and detecting unexpected anomalies. By integrating such methods into the physicist’s toolkit, we show how foundation models can accelerate discovery and open new directions in the exploration of fundamental laws of nature.
| Session Selection | Particle and Nuclear Physics |
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