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Taming redshift-space distortion effects in the EFTofLSS and its application to data by D'Amico, Guido; Senatore, Leonardo; Zhang, Pierre; Nishimichi, Takahiro is a scholarly article available to read on EtoBox.

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Former analyses of the BOSS data using the Effective Field Theory of Large-Scale Structure (EFTofLSS) have measured that the largest counterterms are the redshift-space distortion ones. This allows us to adjust the power-counting rules of the theory, and to explicitly identify that the leading next-order terms have a specific dependence on the cosine of the angle between the line-of-sight and the wavenumber of the observable, $\mu$. Such a specific $\mu$-dependence allows us to construct a linear combination of the data multipoles, $\slashed{P}$, where these contributions are effectively projected out, so that EFTofLSS predictions for $\slashed{P}$ have a much smaller theoretical error and so a much higher $k$-reach. The remaining data are organized in wedges in $\mu$ space, have a $\mu$-dependent $k$-reach because they are not equally affected by the leading next-order contributions, and therefore can have a higher $k$-reach than the multipoles. Furthermore, by explicitly including the highest next-order terms, we define a `one-loop+' procedure, where the wedges have even higher $k$-reach. We study the effectiveness of these two procedures on several sets of simulations and on the

Author

D'Amico, Guido; Senatore, Leonardo; Zhang, Pierre; Nishimichi, Takahiro

Published

2021

Language

EN