Beyond power spectrum to unveil systematics on Hi intensity maps

intensity mapping is a promising technique to probe large-scale structure, traditionally analyzed via two-point statistics such as the angular power spectrum. This latter technique has proven very powerful but may miss key non-Gaussian information present in the signal. We extend the starlet ell₁-norm, a multi-scale higher-order statistic previously applied to weak lensing maps, to the brightness temperature fluctuations of the density field. The signal is highly non-Gaussian at late times (z < 1) due to nonlinear structure growth, motivating the use of advanced summary statistics. We simulated full-sky framework, we performed neural density estimation for implicit likelihood inference. The analysis considered simulated maps incorporating realistic noise and telescope beam, capturing the impact of observational effects on parameter inference. In this work, we focus on the redshift range 0. 4 < z < 0. 45, chosen to match the interval already targeted by existing MeerKLASS observations. We also assess the sensitivity of these statistics to observational systematics, highlighting their potential for identifying and mitigating contaminants in lognormal brightness temperature maps using CAMB and GLASS, generating 10, 000 realizations with associated cosmological parameters. We extracted both the starlet ell₁-norm and angular power spectrum from these maps. Using the JaxILI intensity maps. The starlet ell₁-norm significantly outperforms the angular power spectrum in constraining cosmological parameters, achieving almost a 3x improvement in the figure of merit relative to the angular power spectrum by capturing non-Gaussian features missed by two-point statistics. Moreover, our results suggest that the starlet ell₁-norm is robust to several of the systematic effects included in our simulations. Our findings highlight the potential of multi-scale higher-order statistics such as the starlet ell₁-norm to enhance cosmological inference from future intensity mapping surveys.