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Tracing molecular gas across different ISM environments

Supervisors: Dr. Thomas Bisbas & Prof. Dr. Stefanie Walch-Gassner

A full accounting of molecular hydrogen (H2) in galaxies is fundamental for understanding their star-formation activity and thus their global evolution. Due to quantum mechanical properties, the H2 molecule does not emit radiation that can be captured by radiotelescopes. Astronomers have, therefore, implemented use of other lines, especially low-J CO and dust continuum emission to infer this mass indirectly. Once the CO (J=1-0) line is observed, a scaling factor known as 'conversion factor' or 'Xco-factor' is used to convert its velocity-integrated brightness temperature to H2 column density on scales of molecular clouds or larger.

However, there is growing evidence that this CO-to-H2 method may not be accurate for extragalactic objects, particularly for those with high star-formation rates. Such galaxies are normally found at a redshift of 2-3 when the Universe was just 3 billion years old and when galaxy assembly occurred. Alternative methods are currently examined both theoretically and observationally. In the era of ALMA and SOFIA, the CI-to-H2 and CII-to-H2 as alternative methods are among the most favored.

We have a number of astrochemical models explaining how the carbon cycle phase changes in different environmental ISM parameters (such as the FUV radiation, cosmic-rays, metallicity) in relation to the atomic-to-molecular transition. We have also a set of state-of-the-art three-dimensional models which allow to perform statistical analyses and derive relations between the emission of the molecular/atomic/ionic lines and the column density of molecular gas. The task is to see how the conversion factors based on CO, CI, and CII change as a function of the ISM environmental parameters and how accurate they are in each case. The results of this project will be then applied to existing observational data for which we have access. This project requires a basic background knowledge of ISM physics, knowledge of radiative transfer and excellent computational skills in FORTRAN and Python languages.