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(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.)

Spatial field reconstruction with INLA: application to IFU galaxy data

Texto completo
Gonzalez-Gaitan, S. [1] ; de Souza, R. S. [2] ; Krone-Martins, A. [3] ; Cameron, E. [4] ; Coelho, P. [5] ; Galbany, L. [6] ; Ishida, E. E. O. [7] ; Collaboration, COIN
Número total de Autores: 8
Afiliação do(s) autor(es):
[1] Univ Lisbon, Inst Super Tecn, CENTRA COSTAR, Ave Rovisco Pais 1, P-1049001 Lisbon - Portugal
[2] Univ N Carolina, Dept Phys & Astron, Chapel Hill, NC 27599 - USA
[3] Univ Lisbon, Fac Ciencias, CENTRA SIM, Ed C8, P-1749016 Lisbon - Portugal
[4] Univ Oxford, Li Ka Shing Ctr Hlth Informat & Discovery, Big Data Inst, Old Rd Campus, Oxford OX3 7LS - England
[5] Univ Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, BR-05508090 Sao Paulo, SP - Brazil
[6] Univ Pittsburgh, Dept Phys & Astron, PITT PACC, Pittsburgh, PA 15260 - USA
[7] Univ Clermont Auvergne, CNRS IN2P3, LPC, F-63000 Clermont Ferrand - France
Número total de Afiliações: 7
Tipo de documento: Artigo Científico
Fonte: Monthly Notices of the Royal Astronomical Society; v. 482, n. 3, p. 3880-3891, JAN 2019.
Citações Web of Science: 1

Astronomical observations of extended sources, such as cubes of integral field spectroscopy (IFS), encode autocorrelated spatial structures that cannot be optimally exploited by standard methodologies. This work introduces a novel technique to model IFS data sets, which treats the observed galaxy properties as realizations of an unobserved Gaussian Markov random field. The method is computationally efficient, resilient to the presence of low-signal-to-noise regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian inference - the Integrated Nested Laplace Approximation. As a case study, we analyse 721 IFS data cubes of nearby galaxies from the CALIFA and PISCO surveys, for which we retrieve the maps of the following physical properties: age, metallicity, mass, and extinction. The proposed Bayesian approach, built on a generative representation of the galaxy properties, enables the creation of synthetic images, recovery of areas with bad pixels, and an increased power to detect structures in data sets subject to substantial noise and/or sparsity of sampling. A snippet code to reproduce the analysis of this paper is available in the COIN toolbox, together with the field reconstructions of the CALIFA and PISCO samples. (AU)

Processo FAPESP: 09/54006-4 - Um cluster de computadores para o Departamento de Astronomia do IAG-USP e para o Núcleo de Astrofísica da UNICSUL
Beneficiário:Elisabete Maria de Gouveia Dal Pino
Linha de fomento: Auxílio à Pesquisa - Programa Equipamentos Multiusuários