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Application of Compressed Sensing to Reconstruction of 3-D Charge Collection Efficiency in Silicon Sensors (2016)

Thesis presented in 2016

Link to full pdf version in DUO

Dobos phantom 3um rasterized reconstruction from 12 projections with quasirandom Diamogram RSAF and symmetric diam RSAF.(a) is the reconstruction with quasirandom diamo RSAF and (c) is it projections. (b) the reconstruction with symmetric Diamogram RSAF and (d) is it projections.


CERN is leading the quest of pushing the limits of experimental High Energy Particle Physics
with major upgrades and creating innovative new particle sensors and analysis tools. In this spirit, we have taking up the work done by 3DCCE CERN group where they investigated extending the hit Charge Collection Efficiency characterization of sensors from 2 dimensions to a full spatial 3-D efficiency map. 3DCCE could provide an excellent tool to study local inefficiencies, spacial trapping effects and design impacts of the of 3-D and diamond sensors. The goal of this thesis was to improve the reconstruction algorithm, to reduce the measurements time needed for volumetric reconstruction based on traditional Computed Tomography principles. With the new reconstruction method, known as Compressed Sensing, the measurement time could be reduced from approximately a month to a week or less, fitting better the normal length of a test-beam experiment period. In this thesis, we show the results obtained by using Compressed Sensing to reconstruct phantoms representing Charge Collection Efficiency of double sided 3-D sensors. Furthermore, we optimized one particular implementation of Compressed Sensing, known as reconstruction via Recursive Spatial Adaptive Filtering for those sensors, achieving theoretical reconstruction that points toward a possible measurement time reduction to a day or less.

Emneord: CERN, LHC, ATLAS, Compressed Sensing, detectors
Publisert 11. mars 2021 11:05 - Sist endret 15. mars 2021 14:35


  • Munir Yassin

Omfang (studiepoeng)