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2022

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

Park, Sang Eon, Harris, Philip, Ostdiek, Bryan

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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.

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