What is MCMC?
Markov Chain Monte Carlo (MCMC) is a statistical method used to sample complex, high-dimensional spaces. It generates a sequence of random samples that approximate the target distribution, allowing the computation of integrals, especially when analytical solutions are impractical.
Application in cosmology
In cosmology, MCMC is employed to estimate the likelihood of different models, allowing for the determination of parameters and their uncertainties. It has been applied to data from the cosmic microwave background and galaxy surveys, providing insights into key parameters such as dark matter, dark energy, and the Hubble constant.
Using the weak lensing data from the Kilo-Degree Survey (KiDS), I applied MCMC to measure the parameters of the standard cosmological model, achieving 4.5% precision for $S_8$, a key quantity characterizing cosmic matter. Different feature choices have been carefully investigated and compared to confirm the robustness of my result. I also tested several extended models, searching for hints of new physics.