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Goal-oriented stacking in radio interferometric maps
Dissertation (MSc (Physics))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | en_US |
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University of Pretoria
2022
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| _version_ | 1867613515035967488 |
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| access_status_str | Open Access |
| author2 | Deane, Roger |
| author_browse | Deane, Roger |
| author_facet | Deane, Roger |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Physics))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/88839 |
| institution | University of Pretoria (South Africa) |
| language | en_US |
| last_indexed | 2026-06-10T12:37:22.258Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/88839 Goal-oriented stacking in radio interferometric maps Deane, Roger jacqsmulders@gmail.com Cleghorn, Christopher W. Smulders, Jacques Christian UCTD Interferometry Machine learning Radio optimisation Survey map Algorithm Radio interferometric data Dissertation (MSc (Physics))--University of Pretoria, 2022. Many astrophysical questions are addressed by large area surveys, where sensitivity limits the distance and luminosity of directly detected objects. One way to gain further effective sensitivity is to forego knowledge of individual objects and estimate the aggregate properties of many objects using a technique called stacking. With this approach, images of multiple objects taken from a survey map are used to create a single aggregate image. The typical approach to stacking is to use observed or derived properties in making sub-sample selections in order to seek trends. What has not been explored is designing algorithms to select the sample itself, based on goal-orientated stacking objectives, such as maximising or minimising the aggregate result from stacking. This thesis aims to design a suite of machine learning algorithms that will be employed to select object sub-samples from derived and measured quantities representing each object. This novel approach to statistical inference of radio sources may also have additional applications in statistical self-calibration of radio interferometric data, which would use the same algorithmic framework. ilifu cloud computing facility Physics MSc (Physics) Unrestricted 2022-12-19T08:46:07Z 2022-12-19T08:46:07Z 2023-04 2022 Dissertation * https://repository.up.ac.za/handle/2263/88839 DOI: https://doi.org/10.25403/UPresearchdata.21711194 https://doi.org/10.25403/UPresearchdata.21711194 en_US © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Interferometry Machine learning Radio optimisation Survey map Algorithm Radio interferometric data Goal-oriented stacking in radio interferometric maps |
| title | Goal-oriented stacking in radio interferometric maps |
| title_full | Goal-oriented stacking in radio interferometric maps |
| title_fullStr | Goal-oriented stacking in radio interferometric maps |
| title_full_unstemmed | Goal-oriented stacking in radio interferometric maps |
| title_short | Goal-oriented stacking in radio interferometric maps |
| title_sort | goal oriented stacking in radio interferometric maps |
| topic | UCTD Interferometry Machine learning Radio optimisation Survey map Algorithm Radio interferometric data |
| url | https://repository.up.ac.za/handle/2263/88839 https://doi.org/10.25403/UPresearchdata.21711194 |