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)
en
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.
en
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
en
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
en
dc.subject
neutrinos
en
dc.subject
MicroBooNE
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dc.subject
DUNE
en
dc.subject
machine learning
en
dc.subject
matter and antimatter neutrinos
en
dc.subject
argon atom interaction
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dc.title
Muon neutrino and antineutrino cross sections & neutrino detector readout
en
dc.type
Thesis or Dissertation
en
dc.type.qualificationlevel
Doctoral
en
dc.type.qualificationname
PhD Doctor of Philosophy
en
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