@Article{LimaMaklSouz:2014:BiCoPa,
               author = "Lima, Mariana Penna and Makler, Mart{\'{\i}}n and Souza, Carlos 
                         Alexandre Wuensche de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Centro 
                         Brasileiro de Pesquisas F{\'{\i}}sicas (CBPF)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Biases on cosmological parameter estimators from galaxy cluster 
                         number counts",
              journal = "Journal of Cosmology and Astroparticle Physics",
                 year = "2014",
               volume = "2014",
               number = "05",
                pages = "039--039",
             keywords = "cluster counts, cosmological parameters from LSS.",
             abstract = "Sunyaev-Zeldovich (SZ) surveys are promising probes of cosmology - 
                         in particular for Dark Energy (DE) -, given their ability to find 
                         distant clusters and provide estimates for their mass. However, 
                         current SZ catalogs contain tens to hundreds of objects and 
                         maximum likelihood estimators may present biases for such sample 
                         sizes. In this work we study estimators from cluster abundance for 
                         some cosmological parameters, in particular the DE equation of 
                         state parameter w0, the amplitude of density fluctuations 
                         \σ8, and the Dark Matter density parameter \Ωc. We 
                         begin by deriving an unbinned likelihood for cluster number 
                         counts, showing that it is equivalent to the one commonly used in 
                         the literature. We use the Monte Carlo approach to determine the 
                         presence of bias using this likelihood and study its behavior with 
                         both the area and depth of the survey, and the number of 
                         cosmological parameters fitted. Our fiducial models are based on 
                         the South Pole Telescope (SPT) SZ survey. Assuming perfect 
                         knowledge of mass and redshift some estimators have non-negligible 
                         biases. For example, the bias of \σ8 corresponds to about 
                         40% of its statistical error bar when fitted together with 
                         \Ωc and w0. Including a SZ mass-observable relation 
                         decreases the relevance of the bias, for the typical sizes of 
                         current SZ surveys. Considering a joint likelihood for cluster 
                         abundance and the so-called distance priors, we obtain that the 
                         biases are negligible compared to the statistical errors. However, 
                         we show that the biases from SZ estimators do not go away with 
                         increasing sample sizes and they may become the dominant source of 
                         error for an all sky survey at the SPT sensitivity. Finally, we 
                         compute the confidence regions for the cosmological parameters 
                         using Fisher matrix and profile likelihood approaches, showing 
                         that they are compatible with the Monte Carlo ones. The results of 
                         this work validate the use of the current maximum likelihood 
                         methods for present SZ surveys, but highlight the need for further 
                         studies for upcoming experiments. To perform the analyses of this 
                         work, we developed fast, accurate, and adaptable codes for cluster 
                         counts in the framework of the Numerical Cosmology Library.",
                  doi = "10.1088/1475-7516/2014/05/039",
                  url = "http://dx.doi.org/10.1088/1475-7516/2014/05/039",
                 issn = "1475-7516",
                label = "lattes: 6692996818863210 1 PennaLimaMaklWuen:2014:BiCoPa",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}