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Analysis of pulsating variable stars using the visibility graph algorithm

We study the light curves of pulsating variable stars using a complex network approach to build visibility graphs. We consider various types of variables stars (e.g., Cepheids, δ Scuti, RR Lyrae), build two types of graphs (the normal visibility graph (VG) and the horizontal visibility graph (HVG)),...

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Detalles Bibliográficos
Autores principales: Muñoz, Víctor, Garcés, N. Elizabeth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8598052/
https://www.ncbi.nlm.nih.gov/pubmed/34788340
http://dx.doi.org/10.1371/journal.pone.0259735
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author Muñoz, Víctor
Garcés, N. Elizabeth
author_facet Muñoz, Víctor
Garcés, N. Elizabeth
author_sort Muñoz, Víctor
collection PubMed
description We study the light curves of pulsating variable stars using a complex network approach to build visibility graphs. We consider various types of variables stars (e.g., Cepheids, δ Scuti, RR Lyrae), build two types of graphs (the normal visibility graph (VG) and the horizontal visibility graph (HVG)), and calculate various metrics for the resulting networks. We find that all networks have a power-law degree distribution for the VG and an exponential distribution for the HVG, suggesting that it is a universal feature, regardless of the pulsation features. Metrics such as the average degree, the clustering coefficient and the transitivity coefficient, can distinguish between some star types. We also observe that the results are not strongly affected by the presence of observation gaps in the light curves. These findings suggest that the visibility graph algorithm may be a useful technique to study variability in stars.
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spelling pubmed-85980522021-11-18 Analysis of pulsating variable stars using the visibility graph algorithm Muñoz, Víctor Garcés, N. Elizabeth PLoS One Research Article We study the light curves of pulsating variable stars using a complex network approach to build visibility graphs. We consider various types of variables stars (e.g., Cepheids, δ Scuti, RR Lyrae), build two types of graphs (the normal visibility graph (VG) and the horizontal visibility graph (HVG)), and calculate various metrics for the resulting networks. We find that all networks have a power-law degree distribution for the VG and an exponential distribution for the HVG, suggesting that it is a universal feature, regardless of the pulsation features. Metrics such as the average degree, the clustering coefficient and the transitivity coefficient, can distinguish between some star types. We also observe that the results are not strongly affected by the presence of observation gaps in the light curves. These findings suggest that the visibility graph algorithm may be a useful technique to study variability in stars. Public Library of Science 2021-11-17 /pmc/articles/PMC8598052/ /pubmed/34788340 http://dx.doi.org/10.1371/journal.pone.0259735 Text en © 2021 Muñoz, Garcés https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Muñoz, Víctor
Garcés, N. Elizabeth
Analysis of pulsating variable stars using the visibility graph algorithm
title Analysis of pulsating variable stars using the visibility graph algorithm
title_full Analysis of pulsating variable stars using the visibility graph algorithm
title_fullStr Analysis of pulsating variable stars using the visibility graph algorithm
title_full_unstemmed Analysis of pulsating variable stars using the visibility graph algorithm
title_short Analysis of pulsating variable stars using the visibility graph algorithm
title_sort analysis of pulsating variable stars using the visibility graph algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8598052/
https://www.ncbi.nlm.nih.gov/pubmed/34788340
http://dx.doi.org/10.1371/journal.pone.0259735
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