Speaker
Massimo Guidi
(University of Bologna)
Description
Constraining cosmological parameters for galaxy clustering analyses using the three-point correlation function, despite being pivotal, has historically been limited by the high computational cost of modelling. Here, we introduce a new emulator, based on a convolutional neural network, developed within the framework of a Euclid Preparation Key-Project activity, which substantially accelerates Monte Carlo Markov Chains evaluation making a cosmological analysis feasible. As a result, we will also present how different applications of the new emulator can shed light on disentangling and investigating cosmological models in view of future survey datasets.
Primary author
Massimo Guidi
(University of Bologna)
