Speaker
Description
The characterization of the statistical properties of the cosmological density field represents a central topic in today's cosmology. Understanding the distribution of matter in the Universe provides a powerful means of testing the standard cosmological model and identifying possible deviations. Among the available probes to characterize the statistical properties of the cosmological density field, weak gravitational lensing (WL) has proven particularly effective for constraining departures from General Relativity (GR), being sensitive to the enhanced clustering of matter predicted by modified gravity (MG) theories while being largely unaffected by the uncertainties associated with galaxy bias on small scales.
Traditionally, cosmological information from the Large-Scale Structure (LSS) has been extracted through two-point statistics, such as the two-point correlation function (2PCF) and the power spectrum. However, these estimators are insensitive to non-Gaussianities arising from non-linear gravitational evolution, precisely where MG screening mechanisms are expected to operate. This limitation is especially relevant in light of upcoming Stage IV surveys, such as the Dark Energy Spectroscopic Instrument (DESI) , the Legacy Survey of Space and Time (LSST), and \textit{Euclid}.
In this work, we studied the performance of different higher-order statistics (HOS) when applied to the Hu-Sawicky formulation of $f(R)$ gravity. These models have a widely known degeneracy with the total neutrino masses that counteract the enhancement in matter clustering form MG effects. We relied on convergence maps obtained from the DUSTGRAIN-\textit{pathfinder} N-body simulations. We used both distance metrics between distributions, as well as nonparametric hypothesis tests, to assess the statistical significance of the distance between the distributions of the HOS measured on $\Lambda$CDM and $f(R)$ simulations. A common results was the HOS outperforming the 2PCF in discriminating between cosmologies, even in the presence of shape noise, which is the main source of error in this kind of analysis.