The main goal of this project is to develop a full methodology to produce strong
constraints on cosmological models with the use of datasets that will become
available over the coming decade. More specifically, we will consider a vast class of
models beyond the standard LCDM paradigm, which are of great relevance and
based on different fundamental physics scenarios. Our aim is to build up novel and
powerful theoretical and data analysis tools to possibly detect deviations from the
LCDM predictions or to largely improve over current constraints on parameters
describing them, by exploiting forthcoming Large LSS and GW datasets, such as, e.g.,
Euclid, SKA, Einstein Telescope.
On the theory side, we will develop a framework, based on symmetry-based
approaches (EFT, bootstrap) to produce useful, testable predictions for a large
number of scenarios. On the data analysis side, we will design and implement novel
statistical estimation, data simulation and analysis tools - based on likelihood-freeinference
and machine learning - which will enable us to optimally extract all the
relevant information from future surveys, also considering beyond power spectrum
statistics at non-linear scales and accounting for systematics and spurious
contaminant effects via a simulation-based, forward-modeling approach.
Docente di riferimento
Massimo Pietroni massimo.pietroni@unipr.it
Bibliografia
Bootstrapping Lagrangian Perturbation Theory for the Large Scale Structure
Marco Marinucci (U. Padua, Dept. Phys. Astron. and INFN, Padua), Kevin
Pardede (INFN, Parma), Massimo Pietroni (INFN, Parma)
e-Print: 2405.08413 [astro-ph.CO]
Constraining Primordial Non-Gaussianity from Large Scale Structure with the
Wavelet Scattering Transform
Matteo Peron (U. Parma (main)), Gabriel Jung (Orsay, IAS), Michele Liguori (U. Padua,
Dept. Phys. Astron.), Massimo Pietroni (U. Parma (main))
e-Print: 2403.17657 [astro-ph.CO
