Busca avançada
Ano de início
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

PCA Tomography: how to extract information from data cubes

Texto completo
Steiner, J. E. [1] ; Menezes, R. B. [1] ; Ricci, T. V. [1] ; Oliveira, A. S. [2]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, BR-05508900 Sao Paulo - Brazil
[2] Univ Vale Paraiba, IP&D, BR-12244000 Sao Jose Dos Campos, SP - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: Monthly Notices of the Royal Astronomical Society; v. 395, n. 1, p. 64-75, May 2009.
Área do conhecimento: Ciências Exatas e da Terra - Astronomia
Citações Web of Science: 49
Assunto(s):Galáxias ativas   Espectroscopia óptica   Processamento de imagens

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector's orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge. (AU)

Processo FAPESP: 06/05203-3 - Diferenciação de modelos para LINERs
Beneficiário:Roberto Bertoldo Menezes
Linha de fomento: Bolsas no Brasil - Mestrado
Processo FAPESP: 05/03323-9 - Análise de componentes principais de uma amostra de galáxias Seyferts próximas
Beneficiário:Tiago Vecchi Ricci
Linha de fomento: Bolsas no Brasil - Mestrado