Hashing and metric learning for charged particle tracking

Understand

We propose a novel approach to charged particle tracking at high intensity particle colliders based on Approximate Nearest Neighbors search.

• With hundreds of thousands of measurements per collision to be reconstructed e.g.
• at the High Luminosity Large Hadron Collider, the currently employed combinatorial track finding approaches become inadequate.
• Here, we use hashing techniques to separate measurements into buckets of 20-50 hits and increase their purity using metric learning.