A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS

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dc.contributor.advisorVilalta, Ricardo
dc.contributor.committeeMemberPinsky, Lawrence S.
dc.contributor.committeeMemberShah, Shishir Kirit
dc.contributor.committeeMemberJohnsson, Lennart
dc.contributor.committeeMemberTsekos, Nikolaos V.
dc.creatorHoang, Son M. 1985-
dc.date.accessioned2016-02-15T01:52:01Z
dc.date.available2016-02-15T01:52:01Z
dc.date.createdDecember 2013
dc.date.issued2013-12
dc.date.updated2016-02-15T01:52:01Z
dc.description.abstractThe rapid development in semiconductor detector technology at CERN has provided the capability to develop an active personal dosimeter for use in space radiation environments. The work reported here is based on the Timepix chip, which when coupled with an Si sensor, can function as an active nuclear emulsion, allowing the visualization of the individual tracks created as the different incident particles traverse the detector. The Timepix chip provides the capability of measuring the energy deposited by each incident particle that traverses the sensor layer. Together with the capability for online readout, this detector opens the door to a completely new generation of active Space Radiation Dosimeters. Although recent advances in hardware technology promise a major step forward in the development of such active portable space radiation dosimeters, little effort has been devoted toward software tools for analysis and classification of sources of radiation. Coupling radiation dosimeter hardware with pattern recognition techniques and machine learning tools has the potential to greatly improve current applications on space dosimeter projects. Our focus is not only to measure dosimetric endpoints directly such as dose-equivalent, but also to determine the physical nature of the radiation field with sufficient precision to allow characterization of the radiation composition and energy spectrum.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/1189
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectPattern recognition
dc.subjectMachine learning
dc.subjectCharged particle
dc.subjectTimepix
dc.titleA PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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