This repository contains the code and data files for the PHAS0052 group project. The primary objective is to model and analyze the physical properties of molecular clouds using the Radex radiative transfer code and Markov Chain Monte Carlo (MCMC) methods.
Supervisor: Prof. Mihkel Kama
Studying the physical conditions of interstellar molecular clouds is crucial for understanding star and planet formation. This project leverages computational tools to interpret molecular line emission spectra, which serve as powerful diagnostics for physical parameters like gas density and temperature.
This work is divided into two main components:
- An Independent Study: An exploration of the fundamental principles of radiative transfer, focusing on the Radex library, the structure of LAMDA data files, and an analysis of optical depth and molecular energy levels.
- A Collaborative Investigation: The application of
python-radexin conjunction with MCMC techniques to infer the physical properties of a synthetic molecular cloud from its emission spectrum.
The project utilizes data for A-type methanol (CH₃OH), a key molecule in astrochemistry, with its spectral data provided in the LAMDA format.
project_code.ipynb: A Jupyter Notebook containing the introductory guide to the Radex library and the detailed study on optical depth and energy levels. This represents the independent work completed for the project.mcmc_and_radex.ipynb: A Jupyter Notebook that implements the MCMC analysis usingpython-radexto predict cloud properties. This was a collaborative effort.a_ch3oh.dat: The LAMDA data file for A-type methanol, containing its collisional and radiative transition data.e_ch3oh.dat: The LAMDA data file for E-type methanol, containing its collisional and radiative transition data.co_datafile.dat: The LAMDA data file for CO, containing its collisional and radiative transition data.
- Final Report: Report summarising the most import findings, main focus on optical depth.
- Radex Library Tutorial: A practical introduction on how to use the Radex library for non-LTE (Local Thermodynamic Equilibrium) molecular excitation and radiative transfer calculations.
- LAMDA Data File Analysis: A breakdown of the structure and content of
*.datfiles used by Radex, which contain essential molecular and collisional data. - Optical Depth and Energy Level Study: An in-depth analysis of key physical concepts in radiative transfer, such as line optical depth and its relation to molecular energy level populations.
- MCMC Parameter Estimation: Framework for generating a synthetic molecular cloud spectrum and subsequently using an MCMC algorithm to retrieve the underlying physical parameters (e.g., kinetic temperature, molecular hydrogen density).
- Prof. Mihkel Kama (Project Supervisor)
- The developers of PythonRadex (https://github.com/gica3618/pythonradex), and the Leiden Atomic and Molecular Database (LAMDA).