Similarity hashing for charged particle tracking
- Author(s)
- Sabrina Amrouche, Tobias Golling, Moritz Kiehn, Claudia Plant, Andreas Salzburger
- Abstract
The tracking of charged particles produced in high energy collisions is particularly challenging. The combinatorics approach currently used to track tens of thousands of particles becomes inadequate as the number of simultaneous collisions increase at the High Luminosity Large Hadron Collider (HLLHC). We propose to reduce the complexity of tracking in such dense environments with the use of similarity hashing. We use hashing techniques to separate the detector space into buckets. The particle purity of these buckets is increased using Approximate Nearest Neighbors search. The bucket size is sufficiently small to significantly reduce the complexity of track reconstruction within the buckets. We demonstrate the use of the proposed approach on a public dataset of simulated collisions. The performance evaluation shows a significant speed improvement over the current technique and a further understanding of charged particles structure.
- Organisation(s)
- Research Network Data Science, Research Group Data Mining and Machine Learning
- External organisation(s)
- Université de Genève, European Organization for Nuclear Research (CERN)
- Pages
- 1595-1600
- No. of pages
- 6
- DOI
- https://doi.org/10.1109/BigData47090.2019.9006316
- Publication date
- 2019
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102033 Data mining
- Keywords
- ASJC Scopus subject areas
- Information Systems and Management, Artificial Intelligence, Information Systems, Computer Networks and Communications
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/similarity-hashing-for-charged-particle-tracking(b87f6548-28d8-46d6-ae13-f889a28c66d6).html