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ProtoDUNE data analysis using Python

The Deep Underground Neutrino Experiment is one of the most important experiments for neutrino physics and the prototype ditector is evaluated in C\ ERN. The data analysis of 50 liter prototype experiment was done to do energy calibration using the most probable value of cosmic ray muon and energy\...

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Autor principal: Hikida, Junya
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:http://cds.cern.ch/record/2825374
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author Hikida, Junya
author_facet Hikida, Junya
author_sort Hikida, Junya
collection CERN
description The Deep Underground Neutrino Experiment is one of the most important experiments for neutrino physics and the prototype ditector is evaluated in C\ ERN. The data analysis of 50 liter prototype experiment was done to do energy calibration using the most probable value of cosmic ray muon and energy\ peaks from 207Bismuth source and to reconstruct 3D muon track event. For preparation of analysis, data file was needed to be converted into Pickle f\ ile, and the data for each channel was subtracted baseline and calibrated. As the first analysis, the ADC value was calibrated with the cosmic muon m\ ost probable value was done, and the result was 1.347$\pm$0.035 keV/ADC. On the other hand, energy calibration with source spectrum subtracted backgr\ ound roughly resulted in 1.320$\pm$0.020 keV/ADC, which was consistent with the most probable value calibration result within the error. Lastly, 3D m\ uon track reconstruction was done, and enabled to make 3D plot of muon and delta ray event.
id cern-2825374
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28253742022-08-26T20:48:47Zhttp://cds.cern.ch/record/2825374engHikida, JunyaProtoDUNE data analysis using PythonParticle Physics - ExperimentThe Deep Underground Neutrino Experiment is one of the most important experiments for neutrino physics and the prototype ditector is evaluated in C\ ERN. The data analysis of 50 liter prototype experiment was done to do energy calibration using the most probable value of cosmic ray muon and energy\ peaks from 207Bismuth source and to reconstruct 3D muon track event. For preparation of analysis, data file was needed to be converted into Pickle f\ ile, and the data for each channel was subtracted baseline and calibrated. As the first analysis, the ADC value was calibrated with the cosmic muon m\ ost probable value was done, and the result was 1.347$\pm$0.035 keV/ADC. On the other hand, energy calibration with source spectrum subtracted backgr\ ound roughly resulted in 1.320$\pm$0.020 keV/ADC, which was consistent with the most probable value calibration result within the error. Lastly, 3D m\ uon track reconstruction was done, and enabled to make 3D plot of muon and delta ray event.CERN-STUDENTS-Note-2022-075oai:cds.cern.ch:28253742022-08-26
spellingShingle Particle Physics - Experiment
Hikida, Junya
ProtoDUNE data analysis using Python
title ProtoDUNE data analysis using Python
title_full ProtoDUNE data analysis using Python
title_fullStr ProtoDUNE data analysis using Python
title_full_unstemmed ProtoDUNE data analysis using Python
title_short ProtoDUNE data analysis using Python
title_sort protodune data analysis using python
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2825374
work_keys_str_mv AT hikidajunya protodunedataanalysisusingpython