Neural Embedding: Learning the Embedding of the Manifold of Physics Data

Abstract

In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.

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• Non-resonant Anomaly Detection with Background Extrapolation
• Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
• Anomaly preserving contrastive neural embeddings for end-to-end model-independent searches at the LHC
• Universal New Physics Latent Space