Galaxy clustering analysis with SimBIG and the wavelet scattering transform

The non-Gaussian spatial distribution of galaxies traces the large-scale structure of the Universe and therefore constitutes a prime observable to constrain cosmological parameters. We conduct Bayesian inference of the ΛCDM parameters Ωm, Ωb, h, ns, and σ8 from the Baryon Oscillation Spectroscopic Survey CMASS galaxy sample by combining the wavelet scattering transform (WST) with a simulation-based inference approach enabled by the SimBIG forward model. We design a set of reduced WST statistics that leverage symmetries of redshift-space data. Posterior distributions are estimated with a conditional normalizing flow trained on 20,000 simulated SimBIG galaxy catalogs with survey realism. We assess the accuracy of the posterior estimates using simulation-based calibration and quantify generalization and robustness to the change of forward model using a suite of 2000 test simulations. When probing scales down to kmax=0.5 h/Mpc, we are able to derive accurate posterior estimates that are robust to the change of forward model for all parameters, except σ8. We mitigate the robustness issues with σ8 by removing the WST coefficients that probe scales smaller than k∼0.3 h/Mpc. Applied to the Baryon Oscillation Spectroscopic Survey CMASS sample, our WST analysis yields seemingly improved constraints obtained from a standard perturbation-theory-based power spectrum analysis with kmax=0.25 h/Mpc for all parameters except h. However, we still raise concerns on these results. The observational predictions significantly vary across different normalizing flow architectures, which we interpret as a form of model misspecification. This highlights a key challenge for forward modeling approaches when using summary statistics that are sensitive to detailed model-specific or observational imprints on galaxy clustering. Published by the American Physical Society 2024