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Similarity hashing for charged particle tracking
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...
Autores principales: | , , , , |
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Lenguaje: | eng |
Publicado: |
2019
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1109/BigData47090.2019.9006316 http://cds.cern.ch/record/2827225 |
_version_ | 1780973889407418368 |
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author | Amrouche, Sabrina Golling, Tobias Kiehn, Moritz Plant, Claudia Salzburger, Andreas |
author_facet | Amrouche, Sabrina Golling, Tobias Kiehn, Moritz Plant, Claudia Salzburger, Andreas |
author_sort | Amrouche, Sabrina |
collection | CERN |
description | 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. |
id | cern-2827225 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2019 |
record_format | invenio |
spelling | cern-28272252022-09-16T21:13:04Zdoi:10.1109/BigData47090.2019.9006316http://cds.cern.ch/record/2827225engAmrouche, SabrinaGolling, TobiasKiehn, MoritzPlant, ClaudiaSalzburger, AndreasSimilarity hashing for charged particle trackingComputing and ComputersParticle Physics - ExperimentThe 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.oai:cds.cern.ch:28272252019 |
spellingShingle | Computing and Computers Particle Physics - Experiment Amrouche, Sabrina Golling, Tobias Kiehn, Moritz Plant, Claudia Salzburger, Andreas Similarity hashing for charged particle tracking |
title | Similarity hashing for charged particle tracking |
title_full | Similarity hashing for charged particle tracking |
title_fullStr | Similarity hashing for charged particle tracking |
title_full_unstemmed | Similarity hashing for charged particle tracking |
title_short | Similarity hashing for charged particle tracking |
title_sort | similarity hashing for charged particle tracking |
topic | Computing and Computers Particle Physics - Experiment |
url | https://dx.doi.org/10.1109/BigData47090.2019.9006316 http://cds.cern.ch/record/2827225 |
work_keys_str_mv | AT amrouchesabrina similarityhashingforchargedparticletracking AT gollingtobias similarityhashingforchargedparticletracking AT kiehnmoritz similarityhashingforchargedparticletracking AT plantclaudia similarityhashingforchargedparticletracking AT salzburgerandreas similarityhashingforchargedparticletracking |