Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy

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dc.contributor.advisorCress, Catherineen_ZA
dc.contributor.advisorWinberg, Simonen_ZA
dc.contributor.authorTshililo, Israel Ren_ZA
dc.date.accessioned2016-07-20T06:47:58Z
dc.date.available2016-07-20T06:47:58Z
dc.date.issued2016en_ZA
dc.description.abstractTools to measure clustering are essential for analysis of Astronomical datasets and can potentially be used in other fields for data mining. The Two-point Correlation Function (TPCF), in particular, is used to characterize the distribution of matter and objects such as galaxies in the Universe. However, it's computational time will be restrictively slow given the significant increase in the size of datasets expected from surveys in the future. Thus, new computational techniques are necessary in order to measure clustering efficiently. The objective of this research was to investigate methods to accelerate the computation of the TPCF and to use the TPCF to probe an interesting scientific question dealing with the masses of galaxy clusters measured using data from the Planck satellite. An investigation was conducted to explore different techniques and architectures that can be used to accelerate the computation of the TPCF. The code CUTE, was selected in particular to test shared-memory systems using OpenMP and GPU acceleration using CUDA. Modification were then made to the code, to improve the nearest neighbour boxing technique. The results show that the modified code offers a significant improved performance. Additionally, a particularly effective implementation was used to measure the clustering of galaxy clusters detected by the Planck satellite: our results indicated that the clusters were more massive than had been inferred in previous work, providing an explanation for apparent inconsistencies in the Planck data.en_ZA
dc.identifier.apacitationTshililo, I. R. (2016). <i>Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/20465en_ZA
dc.identifier.chicagocitationTshililo, Israel R. <i>"Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2016. http://hdl.handle.net/11427/20465en_ZA
dc.identifier.citationTshililo, I. 2016. Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Tshililo, Israel R AB - Tools to measure clustering are essential for analysis of Astronomical datasets and can potentially be used in other fields for data mining. The Two-point Correlation Function (TPCF), in particular, is used to characterize the distribution of matter and objects such as galaxies in the Universe. However, it's computational time will be restrictively slow given the significant increase in the size of datasets expected from surveys in the future. Thus, new computational techniques are necessary in order to measure clustering efficiently. The objective of this research was to investigate methods to accelerate the computation of the TPCF and to use the TPCF to probe an interesting scientific question dealing with the masses of galaxy clusters measured using data from the Planck satellite. An investigation was conducted to explore different techniques and architectures that can be used to accelerate the computation of the TPCF. The code CUTE, was selected in particular to test shared-memory systems using OpenMP and GPU acceleration using CUDA. Modification were then made to the code, to improve the nearest neighbour boxing technique. The results show that the modified code offers a significant improved performance. Additionally, a particularly effective implementation was used to measure the clustering of galaxy clusters detected by the Planck satellite: our results indicated that the clusters were more massive than had been inferred in previous work, providing an explanation for apparent inconsistencies in the Planck data. DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy TI - Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy UR - http://hdl.handle.net/11427/20465 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/20465
dc.identifier.vancouvercitationTshililo IR. Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/20465en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleGalaxy evolution, cosmology and HPC : clustering studies applied to astronomyen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Eng)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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