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Pixel Detector Background Generation using Generative Adversarial Networks at Belle II

<!--HTML-->The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background hits on track reconstruction is simulated by adding mea...

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Detalles Bibliográficos
Autor principal: Hashemi, Hosein
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:http://cds.cern.ch/record/2767050
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author Hashemi, Hosein
author_facet Hashemi, Hosein
author_sort Hashemi, Hosein
collection CERN
description <!--HTML-->The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background hits on track reconstruction is simulated by adding measured or simulated background hit patterns to the hits produced by simulated signal particles. This model requires a large set of statistically independent PXD background noise samples to avoid a systematic bias of reconstructed tracks. However, data from the fine-grained PXD requires a substantial amount of storage. As an efficient way of producing background noise, we explore the idea of an on-demand PXD background generator using conditional Generative Adversarial Networks (GANs), adapted by the number of PXD sensors in order to both increase the image fidelity and produce sensor-dependent PXD hitmaps.
id cern-2767050
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
record_format invenio
spelling cern-27670502022-11-02T22:25:41Zhttp://cds.cern.ch/record/2767050engHashemi, HoseinPixel Detector Background Generation using Generative Adversarial Networks at Belle II25th International Conference on Computing in High Energy & Nuclear PhysicsConferences<!--HTML-->The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background hits on track reconstruction is simulated by adding measured or simulated background hit patterns to the hits produced by simulated signal particles. This model requires a large set of statistically independent PXD background noise samples to avoid a systematic bias of reconstructed tracks. However, data from the fine-grained PXD requires a substantial amount of storage. As an efficient way of producing background noise, we explore the idea of an on-demand PXD background generator using conditional Generative Adversarial Networks (GANs), adapted by the number of PXD sensors in order to both increase the image fidelity and produce sensor-dependent PXD hitmaps.oai:cds.cern.ch:27670502021
spellingShingle Conferences
Hashemi, Hosein
Pixel Detector Background Generation using Generative Adversarial Networks at Belle II
title Pixel Detector Background Generation using Generative Adversarial Networks at Belle II
title_full Pixel Detector Background Generation using Generative Adversarial Networks at Belle II
title_fullStr Pixel Detector Background Generation using Generative Adversarial Networks at Belle II
title_full_unstemmed Pixel Detector Background Generation using Generative Adversarial Networks at Belle II
title_short Pixel Detector Background Generation using Generative Adversarial Networks at Belle II
title_sort pixel detector background generation using generative adversarial networks at belle ii
topic Conferences
url http://cds.cern.ch/record/2767050
work_keys_str_mv AT hashemihosein pixeldetectorbackgroundgenerationusinggenerativeadversarialnetworksatbelleii
AT hashemihosein 25thinternationalconferenceoncomputinginhighenergynuclearphysics