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Title: Neural embedding: learning the embedding of the manifold of physics data
A<sc>bstract</sc>

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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Award ID(s):
2019786
NSF-PAR ID:
10488633
Author(s) / Creator(s):
; ;
Publisher / Repository:
For SISSA by Springer
Date Published:
Journal Name:
Journal of High Energy Physics
Volume:
2023
Issue:
7
ISSN:
1029-8479
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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