Effective bias to model and infer the cosmological large scale structure
The cosmological large-scale structure encodes a wealth of information about the origin and evolution of our Universe. Galaxy redshift surveys provide a 3-dimensional picture of the luminous sources in the Universe. These are however biased tracers of the underlying dark matter field. I will discuss the different components which are relevant to model galaxy bias, ranging from deterministic nonlinear, over non-local, to stochastic components. These effective bias ingredients permit us to save computational time and memory requirements, to efficiently produce mock galaxy catalogues. These are useful to study systematics of survey, test analysis tools, and compute covariance matrices to perform a robust analysis of the data. Moreover, this description permits us to implement them in inference analysis methods to recover the dark matter field and its peculiar velocity field. I will show some examples based on the largest sample of luminous red galaxies to date based on the final BOSS SDSS-III data release.
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