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

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. Howe...

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Main Author: Tshililo, Israel R
Other Authors: Cress, Catherine
Format: Thesis
Language:English
Published: Department of Electrical Engineering 2016
Subjects:
Electrical Engineering
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access_status_str Open Access
author Tshililo, Israel R
author2 Cress, Catherine
author_browse Cress, Catherine
Tshililo, Israel R
author_facet Cress, Catherine
Tshililo, Israel R
author_sort Tshililo, Israel R
collection Thesis
description 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.
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institution University of Cape Town (South Africa)
language eng
last_indexed 2026-06-10T12:32:17.361Z
license_str Not specified — see source repository
provenance_str_mv Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository
publishDate 2016
publishDateRange 2016
publishDateSort 2016
publisher Department of Electrical Engineering
publisherStr Department of Electrical Engineering
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source_str UCTD — University of Cape Town Open Access Repository
spelling oai:open.uct.ac.za:11427/20465 Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy Tshililo, Israel R Cress, Catherine Winberg, Simon Electrical Engineering 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. 2016-07-20T06:47:58Z 2016-07-20T06:47:58Z 2016 Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/20465 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town
spellingShingle Electrical Engineering
Tshililo, Israel R
Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy
thesis_degree_str Master's
title Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy
title_full Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy
title_fullStr Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy
title_full_unstemmed Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy
title_short Galaxy evolution, cosmology and HPC : clustering studies applied to astronomy
title_sort galaxy evolution cosmology and hpc clustering studies applied to astronomy
topic Electrical Engineering
url http://hdl.handle.net/11427/20465
work_keys_str_mv AT tshililoisraelr galaxyevolutioncosmologyandhpcclusteringstudiesappliedtoastronomy

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