@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 = "30 abr. 2024"
}