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- W1499161081 abstract "We consider the shape of the posterior distribution to be used when fitting cosmological models to power spectra measured from galaxy surveys. At very large scales, Gaussian posterior distributions in the power do not approximate the posterior distribution $mathcal P_R$ we expect for a Gaussian density field $delta_mathbf{k}$, even if we vary the covariance matrix according to the model to be tested. We compare alternative posterior distributions with $mathcal P_R$, both mode-by-mode and in terms of expected $f_mathrm{NL}$-measurements. Marginalising over a Gaussian posterior distribution $mathcal P_f$ with fixed covariance matrix yields a posterior mean value of $f_mathrm{NL}$ which, for a data set with the characteristics of Euclid, will be underestimated by $triangle f_mathrm{NL}=0.4$, while for the data release 9 (DR9) of the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) it will be underestimated by $triangle f_mathrm{NL}=19.1$. The inverse cubic normal distribution ($mathcal P_mathrm{ICN}$) agrees very well with $mathcal P_R$ at all scales and for all data sets, hence providing the same marginalised value. Adopting this likelihood function means that we do not require a different covariance matrix for each model to be tested: this dependence is absorbed into the functional form of the posterior. Thus, the computational burden of analysis is significantly reduced." @default.
- W1499161081 created "2016-06-24" @default.
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- W1499161081 date "2015-04-15" @default.
- W1499161081 modified "2023-10-14" @default.
- W1499161081 title "Do we need model-dependent covariances when we test cosmological models with galaxy power spectra?" @default.
- W1499161081 hasPublicationYear "2015" @default.
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