CMB-S4—the next-generation ground-based cosmic microwave background (CMB) experiment—is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the universe. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semianalytic projection tool, targeted explicitly toward optimizing constraints on the tensor-to-scalar ratio, r, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2–3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments, given a desired scientific goal. To form a closed-loop process, we couple this semianalytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for r > 0.003 at greater than 5σ, or in the absence of a detection, of reaching an upper limit of r < 0.001 at 95% CL.

The presence of massive neutrinos affects structure formation, leaving imprints on large-scale structure observables such as the weak lensing field. The common lensing analyses with two-point statistics are insensitive to the large amount of non-Gaussian information in the density field. We investigate non-Gaussian tools, in particular the Minkowski Functionals (MFs)-morphological descriptors including area, perimeter, and genus-in an attempt to recover the higher-order information. We use convergence maps from the Cosmological Massive Neutrino Simulations (MassiveNus) and assume galaxy noise, density, and redshift distribution for an LSST-like survey. We show that MFs are sensitive to the neutrino mass sum, and the sensitivity is redshift dependent and is non-Gaussian. We find that redshift tomography significantly improves the constraints on neutrino mass for MFs, compared to the improvements for the power spectrum. We attribute this to the stronger redshift dependence of neutrino effects on small scales. We then build an emulator to model the power spectrum and MFs, and study the constraints on [M ν , Ω m , A s ] from the power spectrum, MFs, and their combination. We show that MFs significantly outperform the power spectrum in constraining neutrino mass, by more than a factor of four. However, a thorough study of the impact from systematics such as baryon physics and galaxy shape and redshift biases will be important to realize the full potential of MFs.

Minkowski Functionals (MF) are excellent tools to investigate the statistical properties of the cosmic background radiation (CMB) maps. Between their notorious advantages is the possibility to use them efficiently in patches of the CMB sphere, which allow studies in masked skies, inclusive analyses of small sky regions. Then, possible deviations from Gaussianity are investigated by comparison with MF obtained from a set of Gaussian isotropic simulated CMB maps to which are applied the same cut-sky masks. These analyses are sensitive enough to detect contaminations of small intensity like primary and secondary CMB anisotropies. Our methodology uses the MF, widely employed to study non-Gaussianities in CMB data, and asserts Gaussian deviations only when all of them points out an exceptional χ 2 value, at more than 2.2 σ confidence level, in a given sky patch. Following this rigorous procedure, we find 13 regions in the foreground-cleaned Planck maps that evince such high levels of non-Gaussian deviations. According to our results, these non-Gaussian contributions show signatures that can be associated to the presence of hot or cold spots in such regions. Moreover, some of these non-Gaussian deviations signals suggest the presence of foreground residuals in those regions located near the galactic plane. Additionally, we confirm that most of the regions revealed in our analyses, but not all, have been recently reported in studies done by the Planck collaboration. Furthermore, we also investigate whether these non-Gaussian deviations can be possibly sourced by systematics, like inhomogeneous noise and beam effect in the released Planck data, or perhaps due to residual galactic foregrounds.