2025 Project Description

Constraining cosmological models with DESI, Planck, and supernovae data

Supervisors

Shouvik Roy Choudhury
Find out more about supervisors on ASIAA website

Task Description and Goals

The project aims to explore the interplay between dark energy and neutrino physics using cutting-edge cosmological datasets, including DESI galaxy data, Planck CMB observations, and Type Ia supernovae data. These datasets provide complementary probes of the Universe's expansion history and structure formation, enabling precise constraints on cosmological parameters. The student will utilize computational tools such as Cobaya, a Bayesian inference framework, and CAMB, a Boltzmann code for calculating cosmological observables, to model and analyze these datasets. The primary objective is to investigate the impact of varying dark energy models and neutrino properties (such as masses and possible self-interactions) on the cosmological parameter space. This involves running Markov Chain Monte Carlo (MCMC) chains to obtain constraints on parameters of the cosmological model and evaluate model likelihoods. Specific tasks include: 1. Familiarizing with Cobaya and CAMB, including setting up simulations and understanding their input/output structures. 2. Incorporating DESI, Planck, and supernovae likelihoods into Cobaya for a joint analysis. 3. Exploring the effects of different dark energy parameterizations (e.g., wCDM, CPL model) and varying neutrino properties on cosmological observables. 4. Comparing constraints on dark energy and neutrino parameters derived from individual datasets versus their combination. 5. Interpreting the results in the context of current cosmological tensions, such as the Hubble tension and S8 tension. By the end of the project, the student will gain hands-on experience with state-of-the-art cosmological analysis tools, develop skills in Bayesian inference and data analysis, and contribute to improving our understanding of dark energy and neutrino physics in the context of precision cosmology.

Required Background

1. Knowledge of General Relativity and Cosmological Perturbation Theory is desirable but not mandatory. 2. Programming knowledge in Python and Fortran is desirable. 3. Basic knowledge of Bayesian statistics is desirable but not mandatory.