Speaker
Description
Higher-order correlation functions (such as the three-point correlation function, 3PCF) will play a crucial role in future cosmological surveys since they complement the information provided by the (more commonly used) lower-order correlation functions (e.g., the two-point correlation function, 2PCF) allowing a significant gain in the cosmological information extracted from Large Scale Structure.
However, one problem relies upon the fact that producing models for the 3PCF is challenging both theoretically, but also computationally, so this has currently become a major bottleneck preventing the possibility of massive 3PCF analyses.
In this talk, I will present a new work in which we developed a Machine-Learning emulator to produce models of the anisotropic 3PCF of matter at BAO scales. We use this model to explore the potential constraints we can have on cosmological parameters with future surveys, and the ability to robustly detect the BAO peak in the 3PCF. I also present a new emulator for the total and no-wiggle power spectrum, which is capable of reproducing the original with an accuracy <1% at all scales. This work is a pilot project that opens the possibility of a full emulation of 3PCF models.
