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We perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the wavelet scattering transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST coefficients and the 2-point correlation function multipoles, trained from the state-of-the-art suite of abacussummit simulations combined with a flexible halo occupation distribution (HOD) galaxy model. In order to confirm the accuracy of our pipeline, we subject it to a series of thorough internal and external mock parameter recovery tests, before applying it to reanalyze the CMASS observations in the redshift range 0. 46<z<0. 57. We find that a joint WST+2-point correlation function likelihood analysis allows us to obtain marginalized 1 errors on the parameters that are tighter by a factor of 2. 5--6, compared to the 2-point correlation function, and by a factor of 1. 4--2. 5 compared to the WST-only results. This corresponds to a competitive 0. 9%, 2. 3% and 1% level of determination for parameters ₂, ₈&nₒ, respectively, and also to a 0. 7% and 2. 5% constraint on derived parameters h and f (z) ₈ (z), in agreement with the Planck 2018 results. Our results reaffirm the constraining power of the WST and highlight the exciting prospect of employing higher-order statistics in order to fully exploit the power of upcoming stage-IV spectroscopic observations.
Valogiannis et al. (Thu,) studied this question.