Speaker
Description
The marked two-point correlation function, which is particularly sensitive to the surrounding environment, offers a promising approach to enhancing the discriminating power in clustering analyses and to potentially detecting modified gravity (MG) signals. In this talk I will present my work that investigates novel marks based on large-scale environment estimates, which also exploit the anti-correlation between objects in low- and high-density regions. This is the first time that the propagation of discreteness effects in marked correlation functions is investigated in depth. The density-dependent marked correlation function estimated from catalogues is affected by shot noise in a non-trivial way. We assess the performance of various marks to distinguish general relativity (GR) from MG. This is achieved through the use of the ELEPHANT suite of simulations, which comprise five realisations of GR and two different MG theories: $f(R)$ and nDGP. In addition, discreteness effects are thoroughly studied using the high-density Covmos catalogues. We have established a robust method to correct for shot-noise effects that can be used in practical analyses. This method allows the recovery of the true signal, with an accuracy below 5% over the scales of $5 \,h^{−1}$ Mpc up to $150 \,h^{−1}$ Mpc. Furthermore, we demonstrate that marks that anti-correlate objects in low- and high-density regions are among the most effective in distinguishing between MG and GR; they also uniquely provide visible deviations on large scales, up to about $80 \,h^{-1}$ Mpc. We report differences in the marked correlation function between $f(R)$ with $|fR0|= 10^{-6}$ and GR simulations of the order of $3–5\sigma$ in real space. The redshift-space monopole of the marked correlation function in this MG scenario exhibits similar features and performance as the real-space marked correlation function.
