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Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction

Coherent muon to electron transition (COMET) experiment is an exclusive beamline for studying charge lepton flavor violation through investigation of neutrinoless muon to electron transition. The present work aims to classify signal electron and background from the truth level data generated from GE...

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Main Author: Ibrahim, Fahmi
Format: Thesis
Language:English
Published: 2022
Subjects:
QC Physics
Online Access:http://eprints.utm.my/id/eprint/101950/1/FahmiIbrahimMFS2022.pdf
http://eprints.utm.my/id/eprint/101950/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149139
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spelling my.utm.1019502023-07-25T09:47:33Z http://eprints.utm.my/id/eprint/101950/ Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction Ibrahim, Fahmi QC Physics Coherent muon to electron transition (COMET) experiment is an exclusive beamline for studying charge lepton flavor violation through investigation of neutrinoless muon to electron transition. The present work aims to classify signal electron and background from the truth level data generated from GEANT4 simulation (MC5 file) using CatBoost algorithm. This data was first simulated in the Integrated Comet Experimental Data User Software Toolkit (ICEDUST) framework to extract electron and background samples of the main COMET detector, CyDet. Both electron and background samples are merged and the detector response towards this sampling are calibrated using the previous MC4 file. Subsequently, the muon stopping region, bunch width effect, overflow of hits, trigger acceptance and occupancy parameters are observed. The data was sanitized by applying energy cut to the energy deposited on cylindrical drift chamber (CDC) and Cherenkov trigger hodoscope (CTH). Four local features (charge deposited on CDC wire, radial distance of hit from muon stopping target (MST), relative time to the trigger signal, and angle of hit from x-axis) and four neighbour features (charge deposited on right wire, charge deposited on left wire, time relative to the trigger signal on right wire, and time relative to the trigger signal on left wire) are calculated. Using these selected features along with CatBoost algorithm, 94.2% of background hits are removed, whereas 93.7% of hits signal are retained. Performance study using confusion matrix and features importance shows that radial distance from MST gives the highest contribution in the classification of signal and background. Application of machine learning in particle physics is very useful in predicting the experimental sensitivities and processing of big data analysis. 2022 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/101950/1/FahmiIbrahimMFS2022.pdf Ibrahim, Fahmi (2022) Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction. Masters thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149139
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QC Physics
spellingShingle QC Physics
Ibrahim, Fahmi
Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction
description Coherent muon to electron transition (COMET) experiment is an exclusive beamline for studying charge lepton flavor violation through investigation of neutrinoless muon to electron transition. The present work aims to classify signal electron and background from the truth level data generated from GEANT4 simulation (MC5 file) using CatBoost algorithm. This data was first simulated in the Integrated Comet Experimental Data User Software Toolkit (ICEDUST) framework to extract electron and background samples of the main COMET detector, CyDet. Both electron and background samples are merged and the detector response towards this sampling are calibrated using the previous MC4 file. Subsequently, the muon stopping region, bunch width effect, overflow of hits, trigger acceptance and occupancy parameters are observed. The data was sanitized by applying energy cut to the energy deposited on cylindrical drift chamber (CDC) and Cherenkov trigger hodoscope (CTH). Four local features (charge deposited on CDC wire, radial distance of hit from muon stopping target (MST), relative time to the trigger signal, and angle of hit from x-axis) and four neighbour features (charge deposited on right wire, charge deposited on left wire, time relative to the trigger signal on right wire, and time relative to the trigger signal on left wire) are calculated. Using these selected features along with CatBoost algorithm, 94.2% of background hits are removed, whereas 93.7% of hits signal are retained. Performance study using confusion matrix and features importance shows that radial distance from MST gives the highest contribution in the classification of signal and background. Application of machine learning in particle physics is very useful in predicting the experimental sensitivities and processing of big data analysis.
format Thesis
author Ibrahim, Fahmi
author_facet Ibrahim, Fahmi
author_sort Ibrahim, Fahmi
title Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction
title_short Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction
title_full Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction
title_fullStr Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction
title_full_unstemmed Coherent Muon to Electron Transition (COMET) phase-i local filtering by catboost algorithm for track reconstruction
title_sort coherent muon to electron transition (comet) phase-i local filtering by catboost algorithm for track reconstruction
publishDate 2022
url http://eprints.utm.my/id/eprint/101950/1/FahmiIbrahimMFS2022.pdf
http://eprints.utm.my/id/eprint/101950/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149139
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score 13.211869

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