Cosmological inference with Euclid

Representative image for thesis Tutors: Benjamin Granett
Lasting: 3 years. PhD thesis.

The ESA Euclid mission launching in summer 2023 will carry out an imaging and spectroscopic survey designed to understand the origin of the accelerated expansion of the Universe, the nature of dark energy and the validity of General Relativity on cosmological scales.

Extracting cosmological information from the galaxy field observed by Euclid can be broken into three steps:
  • compressing the data into a set of summary statistics,
  • modelling the statistics as functions of the parameters of the cosmological model,
  • evaluating the posterior distribution of the parameters given the summary statistics and their covariances.
Analytic modelling is challenging due to the complex nonlinear physics at play on small scales in the galaxy density field. We therefore propose to use N-body dark matter simulations to build forward models of the Euclid observables as a function of the cosmological parameters. In order to add galaxies to the dark matter field, we will apply the methods of subhalo abundance matching and halo occupation distribution modelling.

Due to the heavy reliance on numerical simulations, cosmological inference through forward modelling is particularly demanding in terms of computational resources. A promising direction is to use deep learning algorithms to speed up the inference process based on a limited simulation training set.

The interested student will analyse the spectroscopic redshift dataset from the first year of Euclid data (Data Release 1) in the framework of forward modelling for cosmological inference. The project will make use of available dark matter N-body simulations to interpret and model the observations, and may involve the application of machine learning algorithms.

The student will join the international Euclid Consortium and participate in the galaxy clustering science working group.

This project is integrated in the Milan Euclid cosmology group, including Prof. L. Guzzo (UniMi), Dr. C. Carbone (IASF Milano) and will be supervised by Dr. B. Granett (INAF OA Brera-Merate).