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Baryonic effects are amongst the most severe systematics to the tomographicanalysis of weak lensing data which is the principal probe in many futuregenerations of cosmological surveys like LSST, Euclid etc.. Modeling orparameterizing these effects is essential in order to extract valuableconstraints on cosmological parameters. In a recent paper, Eifler et al. (2015)suggested a reduction technique for baryonic effects by conducting a principalcomponent analysis (PCA) and removing the largest baryonic eigenmodes from thedata. In this article, we conducted the investigation further and addressed twocritical aspects. Firstly, we performed the analysis by separating thesimulations into training and test sets, computing a minimal set of principlecomponents from the training set and examining the fits on the test set. Wefound that using only four parameters, corresponding to the four largesteigenmodes of the training set, the test sets can be fitted thoroughly with anRMS $\sim 0.0011$. Secondly, we explored the significance of outliers, the mostexotic/extreme baryonic scenarios, in this method. We found that excluding theoutliers from the training set results in a relatively bad fit and degraded theRMS by nearly a factor of 3. Therefore, for a direct employment of this methodto the tomographic analysis of the weak lensing data, the principle componentsshould be derived from a training set that comprises adequately exotic butreasonable models such that the reality is included inside the parameter domainsampled by the training set. The baryonic effects can be parameterized as thecoefficients of these principle components and should be marginalized over thecosmological parameter space.

Baryonic effects in cosmic shear tomography: PCA parametrization and importance of extreme baryonic models