With fixed known initial phases, voxel-by-voxel Poisson likelihood on the galaxy number-counts field breaks the f(R)–bias degeneracy that power spectra cannot resolve, with voids and walls driving the gain.
Large-Scale Structure, Theory and Statistics
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abstract
I review the standard paradigm for understanding the formation and evolution of cosmic structure, based on the gravitational instability of dark matter, but many variations on this basic theme are viable. Despite the great progress that has undoubtedly been made, steps are difficult because of uncertainties in the cosmological parameters, in the modelling of relevant physical processes involved in galaxy formation, and perhaps most fundamentally in the relationship between galaxies and the underlying distribution of matter. For the foreseeable future, therefore, this field will be led by observational developments allowing model parameters to be tuned and, hopefully, particular scenarios falsified. In these lectures I focus on two ingredients in this class of models: (i) the role of galaxy bias in interpreting clustering data; and (ii) the statistical properties of the initial fluctuations. In the later case, I discuss some ideas as to how the standard assumption - that the primordial density fluctuations constitute a Gaussian random field - can be tested using measurements galaxy clustering and the cosmic microwave background.
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astro-ph.CO 1years
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Disentangling modified gravity and galaxy bias with field-level inference
With fixed known initial phases, voxel-by-voxel Poisson likelihood on the galaxy number-counts field breaks the f(R)–bias degeneracy that power spectra cannot resolve, with voids and walls driving the gain.