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Emulsion Reconstruction Analysis in SND@LHC

Improving the emulsion reconstruction efficiency in SND@LHC, a compact neutrino experiment in the LHC, is important to ensuring faster data processing and more efficient physics analyses. In this report, I outline different methods to improve emulsion reconstruction efficiency and the signal-to-back...

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Autor principal: Krishnan, Tanvi
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:http://cds.cern.ch/record/2867357
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author Krishnan, Tanvi
author_facet Krishnan, Tanvi
author_sort Krishnan, Tanvi
collection CERN
description Improving the emulsion reconstruction efficiency in SND@LHC, a compact neutrino experiment in the LHC, is important to ensuring faster data processing and more efficient physics analyses. In this report, I outline different methods to improve emulsion reconstruction efficiency and the signal-to-background ratio, analyzed by Monte Carlo studies as well as tests with real emulsion data collected in Emulsion Target 1. Monte Carlo tests have shown that improving the angular alignment of emulsion films can have a significant improvement in efficiency. Additionally, reducing track density from $10^5$ to $10^4$ tracks/cm$^2$ also results in a drastic improvement of efficiency. Removing tracks very close in angle to the collision axis is a potential method to reduce track density while improving the signal-to-background ratio as well. From analysis of the muon Monte Carlo sample, omitting base tracks less than 0.005 rad from the collision axis, I was able to reduce the proportion of background base tracks from passing muons to 78.54% while maintaining 90.83% of the signal base tracks from neutrino interactions. Further analysis with real emulsion data demonstrated it is possible to reduce background by 10.04% while retaining 96.48% of signal base tracks.
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institution Organización Europea para la Investigación Nuclear
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publishDate 2023
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spelling cern-28673572023-08-11T19:45:49Zhttp://cds.cern.ch/record/2867357engKrishnan, TanviEmulsion Reconstruction Analysis in SND@LHCDetectors and Experimental TechniquesParticle Physics - ExperimentImproving the emulsion reconstruction efficiency in SND@LHC, a compact neutrino experiment in the LHC, is important to ensuring faster data processing and more efficient physics analyses. In this report, I outline different methods to improve emulsion reconstruction efficiency and the signal-to-background ratio, analyzed by Monte Carlo studies as well as tests with real emulsion data collected in Emulsion Target 1. Monte Carlo tests have shown that improving the angular alignment of emulsion films can have a significant improvement in efficiency. Additionally, reducing track density from $10^5$ to $10^4$ tracks/cm$^2$ also results in a drastic improvement of efficiency. Removing tracks very close in angle to the collision axis is a potential method to reduce track density while improving the signal-to-background ratio as well. From analysis of the muon Monte Carlo sample, omitting base tracks less than 0.005 rad from the collision axis, I was able to reduce the proportion of background base tracks from passing muons to 78.54% while maintaining 90.83% of the signal base tracks from neutrino interactions. Further analysis with real emulsion data demonstrated it is possible to reduce background by 10.04% while retaining 96.48% of signal base tracks.CERN-STUDENTS-Note-2023-027oai:cds.cern.ch:28673572023-08-11
spellingShingle Detectors and Experimental Techniques
Particle Physics - Experiment
Krishnan, Tanvi
Emulsion Reconstruction Analysis in SND@LHC
title Emulsion Reconstruction Analysis in SND@LHC
title_full Emulsion Reconstruction Analysis in SND@LHC
title_fullStr Emulsion Reconstruction Analysis in SND@LHC
title_full_unstemmed Emulsion Reconstruction Analysis in SND@LHC
title_short Emulsion Reconstruction Analysis in SND@LHC
title_sort emulsion reconstruction analysis in snd@lhc
topic Detectors and Experimental Techniques
Particle Physics - Experiment
url http://cds.cern.ch/record/2867357
work_keys_str_mv AT krishnantanvi emulsionreconstructionanalysisinsndlhc