Studying the stability of molecular clouds is important for understanding the early stages of star-formation. We use the observational data to compare the kinetic motion of gas with the self-gravity to establish if the structure will collapse and form star. However, we were constrained by insufficient data and techniques to be able to get better estimate on these parameters. My thesis consists of various projects with an aim to understand the stability and strucutre of Interstellar medium, and more specifically, star-forming regions.

HOTT (Herschel Optimized Tau and Temperature) maps

I am creating a python based pipeline to generate dust optical depth and temeprature maps using data from Herschel Space Observatory at low resoution (36.4") and high resolution (18.1"). This is done by fitting the Spectral Energy Distribution (SED) to PACS (160 μm) and SPIRE (250, 350 and 500 μm) frequencies. Additionally, I am preforming a detailed analysis on estimating the uncertainty in the Herschel data. This pipeline is being contrusted with an aim to generate maps for any photometic Herschel data taken in Parallel mode. The resulting HOTT maps for Gould Belt regions available on the HOTT website.

Viral Ratio: Direct estimate of Kinetic and Gravitational Energies

To understand the stability of a molecular regions, we compare the gravitational strength with the kinetic motions of the clouds as a virial ratio. This tell us if the cloud is bounded that will lead to collapse or if it's unbounded. Maps of line tracers are used to provide the kinetic information, whereas the gravitational energy is mainly parameterized by the estimated mass and effective radius of the cloud. I am working the model that allow us to get the direct estimation of gravitational and kinetic energy from the projected data with minimal assumptions. This method has been tested on a simulated data. Currently, I am appying this on Heschel dust optical depth maps and ammonia data from Green Bank Ammonia Survey (GAS).

Addtionaly, I have also derived a method that allow us to extract a cloud from a molecular region with higher accuracy using Abel's Transformation. This method take into account the fact that there is a limb-brightening effect when extracting the cloud and allow us to get the better estimate of the mass of the cloud.

Data from Dragonfly Space Observatory of Gould Belt regions

I have taken optical data of multiple molecular clouds of Gould Belt regions using the Dragonfly Telephoto Array, situated in New Mexico. These molecular cloud complexes are part of a group of clouds that are anywhere form 100 to 400pc away from the Sun. Dragonfly is designed for observations of ultra-low surface brightness. It uses filters in r-band and g-band that correspond to wavelengths of 658 nm and 510 nm, respectively. I am currently in the process of calibrating and reducing these dataset. The aim is to do a multiwavelengh analysis of these region by comparing it with the submillimeter data from Herschel and radio data from GAS survey.