A perturbative derivation shows that the large-scale structure likelihood is automatically expressed in terms of the tree-level power spectrum, tree-level bispectrum, and the (2,2) one-loop power spectrum correction.
The Likelihood for LSS: Stochasticity of Bias Coefficients at All Orders
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abstract
In the EFT of biased tracers the noise field $\varepsilon_g$ is not exactly uncorrelated with the nonlinear matter field $\delta$. Its correlation with $\delta$ is effectively captured by adding stochasticities to each bias coefficient. We show that if these stochastic fields are Gaussian (the impact of their non-Gaussianity being subleading on quasi-linear scales anyway) it is possible to resum exactly their effect on the conditional likelihood ${\cal P}[\delta_g|\delta]$ to observe a galaxy field $\delta_g$ given an underlying $\delta$. This resummation allows to take them into account in EFT-based approaches to Bayesian forward modeling. We stress that the resulting corrections to a purely Gaussian conditional likelihood with white-noise covariance are the most relevant on scales where the EFT is under control: they are more important than any non-Gaussianity of the noise $\varepsilon_g$.
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Perturbative Likelihoods for Large-Scale Structure of the Universe
A perturbative derivation shows that the large-scale structure likelihood is automatically expressed in terms of the tree-level power spectrum, tree-level bispectrum, and the (2,2) one-loop power spectrum correction.