NGT WP3 Representation Learning for LHC Triggers

About

**Project Description**: The objective is to use Machine Learning to derive optimal representations of LHC collision events that naturally cluster Standard Model processes while maximizing sensitivity to anomalous signatures beyond the Standard Model (BSM). The project will focus on applying state-of-the-art representation learning methods, including unsupervised (Autoencoder) and self-supervised (BERT, JEPA, VICReg, DINO) learning, to compress high-dimensional LHC event data into descriptive latent spaces for trigger decisions. You will evaluate existing anomaly-detection algorithms and their latent-space representations, design custom training strategies to produce optimal representations, and analyze them in terms of their underlying statistics and correlations with physical observables. Finally, you will leverage NGT resources to study scaling laws and understand how representations evolve with training sample size and model capacity.

**Supervisor**: Sabrina Giorgetti, Maciej Mikolaj Glowacki

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