Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

The characterization and modeling of polarized foregrounds has become a critical issue in the quest for primordial $B$-modes. A typical method to proceed is to factorize and parametrize the spectral properties of foregrounds and their scale dependence (i.e. assuming that foreground spectra are well described everywhere by their sky average). Since in reality foreground properties vary across the Galaxy, this assumption leads to inaccuracies in the model that manifest themselves as biases in the final cosmological parameters (in this case the tensor-to-scalar ratio $r$). This is particularly relevant for surveys over large fractions of the sky, such as the Simons Observatory (SO), where the spectra should be modeled over a distribution of parameter values. Here we propose a method based on the existing ``moment expansion'' approach to address this issue in a power-spectrum-based analysis that is directly applicable in ground-based multi-frequency data. Additionally, the method uses only a small set of parameters with simple physical interpretation, minimizing the impact of foreground uncertainties on the final $B$-mode constraints. We validate the method using SO-like simulated observations, recovering an unbiased estimate of the tensor-to-scalar ratio $r$ with standard deviation $\sigma(r)\simeq0.003$, compatible with official forecasts. When applying the method to the public BICEP2/Keck data, we find an upper bound $r<0.06$ ($95\%\,{\rm C.L.}$), compatible with the result found by BICEP2/Keck when parametrizing spectral index variations through a scale-independent frequency decorrelation parameter. We also discuss the formal similarities between the power spectrum-based moment expansion and methods used in the analysis of CMB lensing.

Abstract:

The characterization and modeling of polarized foregrounds has become a critical issue in the quest for primordial $B$-modes. A typical method to proceed is to factorize and parametrize the spectral properties of foregrounds and their scale dependence (i.e. assuming that foreground spectra are well described everywhere by their sky average). Since in reality foreground properties vary across the Galaxy, this assumption leads to inaccuracies in the model that manifest themselves as biases in the final cosmological parameters (in this case the tensor-to-scalar ratio $r$). This is particularly relevant for surveys over large fractions of the sky, such as the Simons Observatory (SO), where the spectra should be modeled over a distribution of parameter values. Here we propose a method based on the existing ``moment expansion'' approach to address this issue in a power-spectrum-based analysis that is directly applicable in ground-based multi-frequency data. Additionally, the method uses only a small set of parameters with simple physical interpretation, minimizing the impact of foreground uncertainties on the final $B$-mode constraints. We validate the method using SO-like simulated observations, recovering an unbiased estimate of the tensor-to-scalar ratio $r$ with standard deviation $\sigma(r)\simeq0.003$, compatible with official forecasts. When applying the method to the public BICEP2/Keck data, we find an upper bound $r<0.06$ ($95\%\,{\rm C.L.}$), compatible with the result found by BICEP2/Keck when parametrizing spectral index variations through a scale-independent frequency decorrelation parameter. We also discuss the formal similarities between the power spectrum-based moment expansion and methods used in the analysis of CMB lensing.

Abstract:

We examine how the mass assembly of central galaxies depends on their location in the cosmic web. The HORIZON-AGN simulation is analysed at z~2 using the DISPERSE code to extract multi-scale cosmic filaments. We find that the dependency of galaxy properties on large-scale environment is mostly inherited from the (large-scale) environmental dependency of their host halo mass. When adopting a residual analysis that removes the host halo mass effect, we detect a direct and non-negligible influence of cosmic filaments. Proximity to filaments enhances the build-up of stellar mass, a result in agreement with previous studies. However, our multi-scale analysis also reveals that, at the edge of filaments, star formation is suppressed. In addition, we find clues for compaction of the stellar distribution at close proximity to filaments. We suggest that gas transfer from the outside to the inside of the haloes (where galaxies reside) becomes less efficient closer to filaments, due to high angular momentum supply at the vorticity-rich edge of filaments. This quenching mechanism may partly explain the larger fraction of passive galaxies in filaments, as inferred from observations at lower redshifts.

Abstract:

Galactic scale tests have proven to be powerful tools in constraining fundamental physics in previously under-explored regions of parameter space. The astrophysical regime which they probe is inherently complicated, and the inference methods used to make these constraints should be robust to baryonic effects. Previous analyses have assumed simple empirical models for astrophysical noise without detailed calibration or justification. We outline a framework for assessing the reliability of such methods by constructing and testing more advanced baryonic models using cosmological hydrodynamical simulations. As a case study, we use the Horizon-AGN simulation to investigate warping of stellar disks and offsets between gas and stars within galaxies, which are powerful probes of screened fifth forces. We show that the degree of `U'-shaped warping of galaxies is well modelled by Gaussian random noise, but that the magnitude of the gas-star offset is correlated with the virial radius of the host halo. By incorporating this correlation we confirm recent results ruling out astrophysically relevant Hu-Sawicki $f(R)$ gravity, and identify a $\sim 30\%$ systematic uncertainty due to baryonic physics. Such an analysis must be performed case-by-case for future galactic tests of fundamental physics.