Particle Identification with the ND280 Fine Grained Detectors for the T2K Experiment
type: CreativeWork
description: The main goal of this thesis is the development of an algorithm for identification of particles (protons, electrons, muons and pions) with trajectories contained within the Fine Grained Detectors of the T2K (Tokai-to-Kamioka) experiment. This is achieved by constructing a distribution that compares the distance and the energy deposited by a particle travelling in the detectors. Using this distribution, a probability density function is created using simulations for particles interacting in the FGDs. The method of comparison includes the calculation of the pull variable, which gives a measurement of how close a particle being analysed is to a determined particle hypothesis. These pull variables can then be used as an estimate of the likelihood for the particle identification. Tests using this method were performed using an independent set of simulated samples, and also control samples of real protons and muons collected with the experiment. The results show that protons and electrons can be identified successfully from muons and pions. However, the developed particle identification algorithm is unable to discriminate between muons and pions since they behave in a very similar way under the conditions studied.
abstract: The main goal of this thesis is the development of an algorithm for identification of particles (protons, electrons, muons and pions) with trajectories contained within the Fine Grained Detectors of the T2K (Tokai-to-Kamioka) experiment. This is achieved by constructing a distribution that compares the distance and the energy deposited by a particle travelling in the detectors. Using this distribution, a probability density function is created using simulations for particles interacting in the FGDs. The method of comparison includes the calculation of the pull variable, which gives a measurement of how close a particle being analysed is to a determined particle hypothesis. These pull variables can then be used as an estimate of the likelihood for the particle identification. Tests using this method were performed using an independent set of simulated samples, and also control samples of real protons and muons collected with the experiment. The results show that protons and electrons can be identified successfully from muons and pions. However, the developed particle identification algorithm is unable to discriminate between muons and pions since they behave in a very similar way under the conditions studied.
author: Popovski Kolaceke, Anezka
language: en
date published: 2014-09
date modified: 2026-07-24T04:03:27Z
provider: University of Regina
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Faculty of Graduate Studies and Research, University of Regina

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10294/5844

PropertyValue
property ID: DOI
value: 10.82465/3855