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Unsupervised classification of simulated black hole shadows
Dissertation (MSc (Physics))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613567832817664 |
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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 | © 2019 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:2263/82201 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:12.302Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| 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:2263/82201 Unsupervised classification of simulated black hole shadows Deane, Roger micaelamenegaldo@gmail.com Davelaar, Jordy Falcke, Heino Menegaldo, Micaela UCTD Astronomy Dissertation (MSc (Physics))--University of Pretoria, 2022. In April 2019, the Event Horizon Telescope (EHT) Collaboration released the first image of a black hole (BH) shadow. Theoretical models that aim to describe the environments of BHs are complex and highly-dimensional numerical simulations are often needed to outline the problem. While previous work has employed the use of machine learning (ML) algorithms to predict BH shadow model parameters from image data, in this thesis, we assess the suitability of a particular class of ML algorithms, namely self-organising maps (SOM), as a tool to classify simulated BH shadow images. We employ the SOM network PINK, which spatially compares visual input using a flip and rotation invariant similarity measure, to generate a set of representative BH shadow prototypes for a library of simulated images. Using this and the clustered input data parameter distributions, we find that the shadow ring size, which is related to BH mass in the model, is the dominant class determining factor of the images. Other model parameters, especially those that influence the orientation of the shadow on the image plane, were less influential on the clustering given PINK’s flip/rotation invariance. Despite this, PINK may be useful in determining persistent image-plane features of BH shadows for other model parameters, given a constant BH mass, to curate a subset of meaningfully different models that can then be used in more advanced analyses reducing the volume of data needing further consideration. Physics MSc (Physics) Unrestricted 2021-10-20T11:08:59Z 2021-10-20T11:08:59Z 2021 2022 Thesis Menegaldo, MP 2021, Unsupervised classification of simulated black hole shadows, Masters thesis, University of Pretoria, Pretoria, http://hdl.handle.net/2263/82201 A2022 http://hdl.handle.net/2263/82201 en © 2019 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 Astronomy Unsupervised classification of simulated black hole shadows |
| title | Unsupervised classification of simulated black hole shadows |
| title_full | Unsupervised classification of simulated black hole shadows |
| title_fullStr | Unsupervised classification of simulated black hole shadows |
| title_full_unstemmed | Unsupervised classification of simulated black hole shadows |
| title_short | Unsupervised classification of simulated black hole shadows |
| title_sort | unsupervised classification of simulated black hole shadows |
| topic | UCTD Astronomy |
| url | http://hdl.handle.net/2263/82201 |