This workshop is intended for undergraduate/graduate students and researchers interested in learning cosmology and numerical computations using CosmoMC and MontePython. During this workshop, participants will learn:
- Basic cosmology including cosmological perturbation theory.
- Bayesian statistics and Markov Chain Monte Carlo (MCMC) technique for analysing parameter constraints for data.
- The use of CosmoMC with CAMB.
- Utilizing MontePython with CLASS.
- Piyabut Burikham, Chulalongkorn University, Thailand
- Teeraparb Chantavat, Institute for Fundamental Study, Naresuan University, Thailand
- Kamphee Karwan, Institute for Fundamental Study, Naresuan University, Thailand
- Nandan Roy, Centre for Theoretical Physics and Natural Philosophy, Mahidol University, Thailand
- Chakkrit Kaeonikhom, Chiang Mai Rajabhat University, Chiang Mai, Thailand
- Utane Sawangwit, National Astronomical Research Institute of Thailand, Thailand
- Poom Kumam, King Mongkut's University of Technology Thonburi, Thailand
**Limited availability upto 30 participants. The attending participants will be announced at later time. The participants must bring his/her own computer/laptop to the workshop.
CosmoMC (Cosmological Monte Carlo) is a software tool used in cosmology and astrophysics for exploring and analyzing the parameter space of cosmological models. It is primarily employed in the context of Bayesian parameter estimation from cosmological data, particularly in the study of the cosmic microwave background (CMB) radiation, large-scale structure, and other cosmological observables.
CosmoMC utilizes Markov Chain Monte Carlo (MCMC) techniques to sample the parameter space of cosmological models and estimate the likelihood of these models given the observed data. This enables researchers to constrain and study the values of various cosmological parameters, such as the density of dark matter, dark energy, and the spectral index of primordial fluctuations. It is often used in conjunction with other cosmological analysis tools like CAMB (Code for Anisotropies in the Microwave Background) for calculating theoretical predictions.
Researchers use CosmoMC to compare theoretical cosmological models with observational data, helping to refine our understanding of the universe's properties and evolution. It's a valuable tool for performing Bayesian parameter estimation in the field of cosmology.
MontePython is a software tool used in the field of cosmology for conducting Bayesian parameter estimation from cosmological data. It is similar in function to CosmoMC, which I mentioned earlier. MontePython uses Markov Chain Monte Carlo (MCMC) techniques to sample the parameter space of cosmological models and estimate the likelihood of these models given observed data.
Researchers in cosmology and astrophysics use MontePython to analyze a wide range of cosmological data, such as data from cosmic microwave background (CMB) experiments, large-scale structure surveys, and other astrophysical observations. The tool allows for the exploration and constraints of various cosmological parameters, helping researchers to determine the best-fitting models that describe the universe's properties and evolution.
MontePython is a flexible and customizable software package that can be adapted to different cosmological models and datasets. It provides a framework for performing advanced statistical analyses and comparing theoretical models to observational data, contributing to our understanding of the cosmos and the fundamental parameters that describe it.