skip to main content
Visitante
Meu Espaço
Minha Conta
Sair
Identificação
This feature requires javascript
Tags
Revistas Eletrônicas (eJournals)
Livros Eletrônicos (eBooks)
Bases de Dados
Bibliotecas USP
Ajuda
Ajuda
Idioma:
Inglês
Espanhol
Português
This feature required javascript
This feature requires javascript
Primo Search
Busca Geral
Busca Geral
Acervo Físico
Acervo Físico
Produção Intelectual da USP
Produção USP
Search For:
Clear Search Box
Search in:
Busca Geral
Or hit Enter to replace search target
Or select another collection:
Search in:
Busca Geral
Busca Avançada
Busca por Índices
This feature requires javascript
This feature requires javascript
Machine learning for radio galaxy morphology analysis
Mostert, R.I.J.
2024
Texto completo disponível
Citações
Citado por
Exibir Online
Detalhes
Resenhas & Tags
Mais Opções
Nº de Citações
This feature requires javascript
Enviar para
Adicionar ao Meu Espaço
Remover do Meu Espaço
E-mail (máximo 30 registros por vez)
Imprimir
Link permanente
Referência
EasyBib
EndNote
RefWorks
del.icio.us
Exportar RIS
Exportar BibTeX
This feature requires javascript
Título:
Machine learning for radio galaxy morphology analysis
Autor:
Mostert, R.I.J.
Assuntos:
Catalogues
;
Galaxies: active
;
Galaxies: peculiar
;
Methods: data analysis
;
Methods: statistical
;
Radio continuum: galaxies
;
Surveys
;
Techniques: computer vision
Descrição:
We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.We investigated what morphology in total radio intensity maps can tell us about observed radio sources without complementary wavelength information and with limited visual inspection. We used a self-organising map (SOM) to model common radio morphologies and to reveal the rarest morphologies in LoTSS.Furthermore, we turned the radio source-component association problem into an object detection problem and trained an adapted Fast region convolutional neural network to mimic the grouping of source components into unique sources as performed by astronomers for LoTSS data.We also reduced the visual inspection required to find RLAGN remnant candidates based on their morphology, by using SOM-based features as input for a random forest classifier.Finally, we created a machine learning pipeline to identify giant radio galaxy (GRG) candidates and created a sample that contains more than ten thousand GRG. We then quantified the intrinsic GRG proper length distribution, the comoving GRG number density, and a current-day GRG lobe volume-filling fraction in clusters and filaments of the Cosmic Web.
Data de criação/publicação:
2024
Idioma:
Inglês
Links
View record in Leiden University Repository$$FView record in $$GLeiden University Repository
This feature requires javascript
This feature requires javascript
Voltar para lista de resultados
This feature requires javascript
This feature requires javascript
Buscando em bases de dados remotas. Favor aguardar.
Buscando por
em
scope:(USP_VIDEOS),scope:("PRIMO"),scope:(USP_FISICO),scope:(USP_EREVISTAS),scope:(USP),scope:(USP_EBOOKS),scope:(USP_PRODUCAO),primo_central_multiple_fe
Mostrar o que foi encontrado até o momento
This feature requires javascript
This feature requires javascript