In this thesis, we present distance measurements to embedded stars by using the wealth of astrometric data from Gaia DR2. Our methodology is based on the Bayesian techniques and the Markov chain Monte Carlo (MCMC) sampling by modelling the extinction towards the region of interest to infer the distance to the target source. We model the Ag extinction in the line of sight to provide reliable distance measurements. We also use the Av extinction derived by Anders et al. (2019) to see the improvement in the distances when using additional catalogues. The distance is subsequently inferred from the jump point on the extinction from the Off-cloud to On-cloud stars as each extinction measurement has its corresponding distance.
We inferred distances to Young Stellar Objects (YSOs) selected from the literature and to the sub-regions of the high mass star formation region, Cygnus X (DR20, DR21, DR22, DR23, and W75N). We found that Gaia can provide a reliable distance to an object associated with a molecular cloud with moderate-sized extinction, showing a small systematic uncertainty of less than 5%. For dark clouds, however, our extinction models inferred lower distances compared to maser distances, kinematic distances, and the extinction distances of Foster et al. (2012). This is because there are multiple extinction breakpoints towards those selected regions, and our models provide distances to the first jump. We also found that the sub-regions of Cygnus X are located at a similar distance of ∼1kpc according to Ag, and at ∼1.6kpc according to Av. This suggests that the idea of using additional photometric data with Gaia in the Av model improves the distance as it added many input stars for the models. Our methodology failed to measure distances to object in a cloud with complex extinction distribution that differs from our simple dust screen model. We stress, however, that the advent of the full Gaia Data Release 3 will significantly improve our distance measurements as many more data will be available
