Edinburgh Research Archive

Muon neutrino and antineutrino cross sections & neutrino detector readout

dc.contributor.advisor
Muheim, Franz
dc.contributor.advisor
Nebot-Guinot, Miquel
dc.contributor.advisor
Clark, Philip
dc.contributor.advisor
Williams, Mark
dc.contributor.author
Batchelor, Charlie
dc.contributor.sponsor
Science and Technology Facilities Council (STFC)
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dc.date.accessioned
2025-03-10T11:15:09Z
dc.date.available
2025-03-10T11:15:09Z
dc.date.issued
2025-03-10
dc.description.abstract
The Micro-scale Booster Neutrino Experiment is a ground-level Liquid Argon Time Projection Chamber neutrino detector, which collected data from the Booster Neutrino Beam and coincidentally, the Neutrinos at the Main Injector accelerator neutrino beam at Fermilab. The Deep Underground Neutrino Experiment is a next generation neutrino detector experiment that aims to provide answers to several critical questions posed by the current Standard Model of particle physics. This thesis will explore the use of Machine Learning techniques to extract interesting physics events from Liquid Argon Time Projection Chambers. It presents an investigation into the online triggering of data readout, using a prototype DUNE far detector, based at CERN. An attempt to separate an admixture of muon neutrinos and antineutrinos arriving at the MicroBooNE detector from the NuMI flux is also presented. The analysis then proceeds to extract charged current cross sections for the separated neutrino and antineutrino charged current interactions on argon nucleons. An understanding of these interactions is critical to a successful DUNE physics campaign.
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dc.identifier.uri
https://hdl.handle.net/1842/43187
dc.identifier.uri
http://dx.doi.org/10.7488/era/5728
dc.language.iso
en
en
dc.publisher
The University of Edinburgh
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dc.relation.hasversion
Performance of a Modular Ton-Scale Pixel-Readout Liquid Argon Time Projection Chamber on behalf of the DUNE Collaboration, 11 Sept 2024, In: Instruments. 8, 3, 41
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dc.subject
neutrinos
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dc.subject
MicroBooNE
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dc.subject
DUNE
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dc.subject
machine learning
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dc.subject
matter and antimatter neutrinos
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dc.subject
argon atom interaction
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dc.title
Muon neutrino and antineutrino cross sections & neutrino detector readout
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Doctoral
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dc.type.qualificationname
PhD Doctor of Philosophy
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