Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology
Fast radio bursts (FRBs) represent one of the most intriguing phenomena in modern astrophysics. However, their classification into repeaters and nonrepeaters is challenging. Here, we present the application of the graph theory minimum spanning tree (MST) methodology as an unsupervised classifier of...
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| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad9020 |
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| author | C. R. García Diego F. Torres Jia-Ming Zhu-Ge Bing Zhang |
| author_facet | C. R. García Diego F. Torres Jia-Ming Zhu-Ge Bing Zhang |
| author_sort | C. R. García |
| collection | DOAJ |
| description | Fast radio bursts (FRBs) represent one of the most intriguing phenomena in modern astrophysics. However, their classification into repeaters and nonrepeaters is challenging. Here, we present the application of the graph theory minimum spanning tree (MST) methodology as an unsupervised classifier of repeater and nonrepeater FRBs. By constructing MSTs based on various combinations of variables, we identify those that lead to MSTs that exhibit a localized high density of repeaters at each side of the node with the largest betweenness centrality. Comparing the separation power of this methodology against known machine learning methods, and with the random expectation results, we assess the efficiency of the MST-based approach to unravel the physical implications behind the graph pattern. We finally propose a list of potential repeater candidates derived from the analysis using the MST. |
| format | Article |
| id | doaj-art-9784f827ca0d42648aaf07fc7f4f1174 |
| institution | OA Journals |
| issn | 1538-4357 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-9784f827ca0d42648aaf07fc7f4f11742025-08-20T01:58:08ZengIOP PublishingThe Astrophysical Journal1538-43572024-01-01977227310.3847/1538-4357/ad9020Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised MethodologyC. R. García0https://orcid.org/0000-0002-2437-6331Diego F. Torres1https://orcid.org/0000-0002-1522-9065Jia-Ming Zhu-Ge2https://orcid.org/0000-0001-8114-3094Bing Zhang3https://orcid.org/0000-0002-9725-2524Institute of Space Sciences (ICE, CSIC) , Campus UAB, Carrer de Can Magrans s/n, 08193 Barcelona, Spain ; crodriguez@ice.csic.es; Institut d’Estudis Espacials de Catalunya (IEEC) , 08034 Barcelona, SpainInstitute of Space Sciences (ICE, CSIC) , Campus UAB, Carrer de Can Magrans s/n, 08193 Barcelona, Spain ; crodriguez@ice.csic.es; Institut d’Estudis Espacials de Catalunya (IEEC) , 08034 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA) , E-08010 Barcelona, SpainNevada Center for Astrophysics, University of Nevada , Las Vegas, NV 89154, USA; Department of Physics and Astronomy, University of Nevada , Las Vegas, NV 89154, USANevada Center for Astrophysics, University of Nevada , Las Vegas, NV 89154, USA; Department of Physics and Astronomy, University of Nevada , Las Vegas, NV 89154, USAFast radio bursts (FRBs) represent one of the most intriguing phenomena in modern astrophysics. However, their classification into repeaters and nonrepeaters is challenging. Here, we present the application of the graph theory minimum spanning tree (MST) methodology as an unsupervised classifier of repeater and nonrepeater FRBs. By constructing MSTs based on various combinations of variables, we identify those that lead to MSTs that exhibit a localized high density of repeaters at each side of the node with the largest betweenness centrality. Comparing the separation power of this methodology against known machine learning methods, and with the random expectation results, we assess the efficiency of the MST-based approach to unravel the physical implications behind the graph pattern. We finally propose a list of potential repeater candidates derived from the analysis using the MST.https://doi.org/10.3847/1538-4357/ad9020Radio transient sourcesAstronomy data analysis |
| spellingShingle | C. R. García Diego F. Torres Jia-Ming Zhu-Ge Bing Zhang Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology The Astrophysical Journal Radio transient sources Astronomy data analysis |
| title | Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology |
| title_full | Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology |
| title_fullStr | Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology |
| title_full_unstemmed | Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology |
| title_short | Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology |
| title_sort | separating repeating fast radio bursts using the minimum spanning tree as an unsupervised methodology |
| topic | Radio transient sources Astronomy data analysis |
| url | https://doi.org/10.3847/1538-4357/ad9020 |
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