![Bites of FM4S: [1] Physics-inspired representations](/event/1473554/logo-3452117835.png){kind=logo}
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Description
Discovering the occurrence of unexpected physical processes in collider data could unveil new fundamental laws governing our Universe. However, the extreme size, rate and complexity of the datasets generated at the Large Hadron Collider (LHC) pose unique challenges to detect them. Typically, this is addressed by transforming high-dimensional, low-level detector data into physically meaningful summary statistics, like particle invariant masses. In this work we address the problem of data reduction for anomaly detection by learning powerful new lower dimensional representations of the data via neural embeddings that preserve the anomalous features.
We consider synthetic LHC data originally represented by the kinematic variables of 19 physics objects produced in a collision event, and we study different MLP- and Transformer-based neural embeddings trained according to supervised or self-supervised contrastive learning methods. The learnt embeddings are used as input representation to signal-agnostic statistical detection tools, showing increased detection performances compared to the use of the original representation.