Turbulent entrainment origin of protostellar outflows

Protostellar outflow is a prominent process that accompanies the formation of stars. It is generally agreed that wide-angled protostellar outflows come from the interaction between the wind from a forming star and the ambient gas. However, it is still unclear how the interaction takes place. In this work, we theoretically investigate the possibility that the outflow results from interaction between the wind and the ambient gas in the form of turbulent entrainment. In contrast to the previous models, turbulent motion of the ambient gas around the protostar is taken into account. In our model, the ram-pressure of the wind balances the turbulent ram-pressure of the ambient gas, and the outflow consists of the ambient gas entrained by the wind. The calculated outflow from our modelling exhibits a conical shape. The total mass of the outflow is determined by the turbulent velocity of the envelope as well as the outflow age, and the velocity of the outflow is several times higher than the velocity dispersion of the ambient gas. The outflow opening angle increases with the strength of the wind and decreases with the increasing ambient gas turbulence. The outflow exhibits a broad line width at every position. We propose that the turbulent entrainment process, which happens ubiquitously in nature, plays a universal role in shaping protostellar outflows.Comment: 15 pages, accepted for publication in A&

Model Selection Using Gaussian Mixture Models and Parallel Computing

In recent years, model selection methods have seen significant advancement, but improvements have tended to be bench marked on its efficiency. An effective model selection system requires a robust feature extraction module. A model selection system is developed by using Finite Multivariate Generalized Gaussian Mixture Model, which organize data points to clusters. Clustering is basically to assign data set into different groups based on their similarity. In this model, expectation maximization method is used to calculate the distance from each point to their dummy center point, where center point will be changing with the process of simulation to get the best fitting results. Parallel computing is utilized to accelerate simulation process. The performance of the developed model is studied through experimental evaluation with ten thousands data points and identification accuracy. The system still can be improved by a new algorithm to separate the cluster. Performance evaluations will be investigated and compared