GPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle Tracks

Nuclear emulsions as tracking devices have been used by recent experiments thanks to fast automatic microscopes for emulsion readout. Automatic systems are evolving towards GPU-based solutions. Real-time imaging is needed to drive the motion of the microscope axes and 3D track recognition occurs qua...

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Detalles Bibliográficos
Autores principales: Bozza, Cristiano, Kose, Umut, De Sio, Chiara, Stellacci, Simona Maria
Lenguaje:eng
Publicado: 2015
Materias:
Detectors and Experimental Techniques
Computing and Computers
Acceso en línea:https://dx.doi.org/10.3204/DESY-PROC-2014-05/2
http://cds.cern.ch/record/2043871
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author Bozza, Cristiano
Kose, Umut
De Sio, Chiara
Stellacci, Simona Maria
author_facet Bozza, Cristiano
Kose, Umut
De Sio, Chiara
Stellacci, Simona Maria
author_sort Bozza, Cristiano
collection CERN
description Nuclear emulsions as tracking devices have been used by recent experiments thanks to fast automatic microscopes for emulsion readout. Automatic systems are evolving towards GPU-based solutions. Real-time imaging is needed to drive the motion of the microscope axes and 3D track recognition occurs quasi-online in local GPU clusters. The algorithms implemented in the Quick Scanning System are sketched. Most of them are very general and might turn out useful for other detector
id oai-inspirehep.net-1386617
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
record_format invenio
spelling oai-inspirehep.net-13866172019-09-30T06:29:59Zdoi:10.3204/DESY-PROC-2014-05/2http://cds.cern.ch/record/2043871engBozza, CristianoKose, UmutDe Sio, ChiaraStellacci, Simona MariaGPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle TracksDetectors and Experimental TechniquesComputing and ComputersNuclear emulsions as tracking devices have been used by recent experiments thanks to fast automatic microscopes for emulsion readout. Automatic systems are evolving towards GPU-based solutions. Real-time imaging is needed to drive the motion of the microscope axes and 3D track recognition occurs quasi-online in local GPU clusters. The algorithms implemented in the Quick Scanning System are sketched. Most of them are very general and might turn out useful for other detectoroai:inspirehep.net:13866172015
spellingShingle Detectors and Experimental Techniques
Computing and Computers
Bozza, Cristiano
Kose, Umut
De Sio, Chiara
Stellacci, Simona Maria
GPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle Tracks
title GPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle Tracks
title_full GPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle Tracks
title_fullStr GPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle Tracks
title_full_unstemmed GPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle Tracks
title_short GPU-based quasi-real-time Track Recognition in Imaging Devices: from raw Data to Particle Tracks
title_sort gpu-based quasi-real-time track recognition in imaging devices: from raw data to particle tracks
topic Detectors and Experimental Techniques
Computing and Computers
url https://dx.doi.org/10.3204/DESY-PROC-2014-05/2
http://cds.cern.ch/record/2043871
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AT koseumut gpubasedquasirealtimetrackrecognitioninimagingdevicesfromrawdatatoparticletracks
AT desiochiara gpubasedquasirealtimetrackrecognitioninimagingdevicesfromrawdatatoparticletracks
AT stellaccisimonamaria gpubasedquasirealtimetrackrecognitioninimagingdevicesfromrawdatatoparticletracks

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