@Article{NovaesWuen:2012:IdGaCl,
author = "Novaes, Camila Paiva and Wuensche, Carlos Alexandre",
affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Identification of galaxy clusters in cosmic microwave background
maps using the Sunyaev-Zel'dovich effect",
journal = "Astronomy \& Astrophysics",
year = "2012",
volume = "545",
number = "A34",
pages = "A34",
keywords = "galaxy clusters, simulations, independent component analysis,
blind separation.",
abstract = "The Planck satellite was launched in 2009 by the European Space
Agency to study the properties of the cosmic microwave background
(CMB). An expected result of the Planck data analysis is the
distinction of the various contaminants of the CMB signal. Among
these contaminants is the Sunyaev-Zel'dovich (SZ) effect, which is
caused by the inverse Compton scattering of CMB photons by high
energy electrons in the intracluster medium of galaxy clusters. We
modify a public version of the JADE (Joint Approximate
Diagonalization of Eigenmatrices) algorithm, to deal with noisy
data, and then use this algorithm as a tool to search for SZ
clusters in two simulated datasets. Methods. The first dataset is
composed of simple {"}homemade{"} simulations and the second of
full sky simulations of high angular resolution, available at the
LAMBDA (Legacy Archive for Microwave Background Data Analysis)
website. The process of component separation can be summarized in
four main steps: (1) pre-processing based on wavelet analysis,
which performs an initial cleaning (denoising) of data to minimize
the noise level; (2) the separation of the components (emissions)
by JADE; (3) the calibration of the recovered SZ map; and (4) the
identification of the positions and intensities of the clusters
using the SExtractor software. The results show that our
JADE-based algorithm is effective in identifying the position and
intensity of the SZ clusters, with the purities being higher then
90% for the extracted {"}catalogues{"}. This value changes
slightly according to the characteristics of noise and the number
of components included in the input maps. The main highlight of
our developed work is the effective recovery rate of SZ sources
from noisy data, with no a priori assumptions. This powerful
algorithm can be easily implemented and become an interesting
complementary option to the {"}matched filter{"} algorithm
(hereafter MF) widely used in SZ data analysis.",
doi = "10.1051/0004-6361/201118482",
url = "http://dx.doi.org/10.1051/0004-6361/201118482",
issn = "0004-6361 and 1432-0746",
label = "lattes: 9310663564448564 1 NovaesWuen:2012:IdGaCl",
language = "pt",
targetfile = "1211.5843v1.pdf",
urlaccessdate = "02 maio 2024"
}