Bennieston, Andrew J. (2013) Reconstruction techniques for fine-grained neutrino detectors. PhD thesis, University of Warwick.

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Abstract

The use of Liquid Argon Time-Projection Chambers in neutrino physics is looking
increasingly certain as the field moves to larger, finer-grained detectors capable of
delivering the physics reach required for the next generation of experiments studying
neutrino oscillations and CP violation in the lepton sector.
This thesis explores reconstruction procedures for use in a Liquid Argon
Time-Projection Chamber (LAr TPC). Fully automated reconstruction of neutrino
events in these environments has not been successfully demonstrated previously,
although several collaborations across the world are working towards this goal. A
number of algorithms and techniques are discussed, and their applicability to the
field of reconstruction in fine-grained detector environments is assessed.
The techniques presented here are fully automated and characterised to the
maximum extent possible, and may be combined to produce a software reconstruction
chain that is free from human intervention. In addition to these algorithms, a
framework for running chained reconstruction tasks is presented and demonstrated
to work in conjunction with the algorithms developed.
Muon identification is also presented, using cuts justified from the truth
information available from simulations. The algorithms and cuts together are used to
analyse simulated neutrino events throughout the thesis, focusing on charged current
muon neutrino interactions at energies of 0:77 GeV and 4:5 GeV, and considering
interactions producing u + p (referred to as CCQE) or u + p + n+ (referred to as
CC1n) final states.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QC Physics
Library of Congress Subject Headings (LCSH):	Neutrinos, Time projection chambers (Nuclear physics)
Official Date:	October 2013
Dates:	Date Event October 2013 Submitted
Institution:	University of Warwick
Theses Department:	Department of Physics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Ramachers, Yorck
Extent:	xiv, 135 leaves : illustrations, charts.
Language:	eng

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