Chamani Gunasekera

Project 1: Elements condensing into dust grains is an important physical process that occurs in the ISM. Through studies on extinction, scattering, heating and depletion of elements out of gas-phase, it has long been known that grains have a major effect on observed spectra of galaxies. Despite the large amount of observations available on extinction, scattering, and heating, details of depletions onto grains and their effect on observed emission-line spectra are rarely studied. My work involves, exploring the effects of grain depletions on strong spectral lines. In addition, we are in the process of adding new code into Cloudy, allowing a user to alter the degree of grain depletions in a given model.

Project 2: Emission-line spectra are produced in ionized plasmas when light interacts with matter through microphysical processes. Hence atomic data are required for the microphysical calculations conducted to simulate these plasmas. One of the atomic and molecular databases utilized by the \Cloudy code was CHIANTI version 7 containing important electron excitation data that Cloudy requires for its calculations. However, as more accurate observations and improved atomic models have become available, newer version of CHIANTI have become available. Since the structure of CHIANTI data has evolved with each new version, the newest CHIANTI 10 version had not been used by Cloudy.
My solution was to develop a Python script to recast data from the latest version of CHIANTI to a common format used by other codes. Due to the large size of this atomic database, such a task required the implementation of parallel computing, dynamic data interpolations, error calculations, and data analytics. Such a solution allows Cloudy to keep up with future changes to the CHIANTI database with minimal changes to my Python script, which is now available online (https://gitlab.nublado.org/arrack).

Project 3: The majority of the baryonic matter in our universe exists as highly non-equilibrium astrophysical plasma. So they are crucial to our understanding of all subfields within astronomy. Astrophysical plasmas can be studied in two newly opened wavelength regimes: IR to near-IR spectra for which the new telescope is the James Webb Space Telescope (JWST), and X-ray spectra, for which the upcoming mission (XRISM) uses microcalorimeter technology.
The field of astronomy is also set to enter an era of big data, with the launch of XRISM, Athena, LSST, and many other missions, which will gather a vast number of observations with unprecedented resolution and accuracy.
This data will bring to light the physics of our universe that has thus far remained elusive.
For example, during the short life of the Hitomi mission, it obtained unprecedented high-resolution X-ray microcalorimeter observations of the Perseus cluster. These data revealed the immense potential of high-resolution X-ray spectroscopy as a probe into the mechanisms within astrophysical plasmas and other extreme environments. Similarly, JWST has displayed the importance of IR and near IR spectroscopy with its ability to resolve dust-obscured galaxy clusters and observe the first stars and galaxies.
However, to understand these new data, we are required to build tools that can predict spectra at the same resolution. This is the drive for my current research project.