  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 \textsc{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$\sigma$ errors on the $\Lambda$CDM 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
$\omega_c$, $\sigma_8$ $\&$ $n_s$, respectively, and also to a $0.7\%$ $\&$
$2.5 \%$ constraint on derived parameters h and $f(z)\sigma_8(z)$, in agreement
with the \textit{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.
