Dependence of star formation rate on different properties of molecular clouds

Properties of molecular clouds ($MCs$) lying in our Galaxy and their star formation scenarios have been investigated with the help of multivariate unsupervised machine learning techniques concerning several observable parameters. At first, the $MCs$ have been classified into four coherent groups using the standard K-means clustering method. Subsequently, the optimum number of groups has been estimated by applying the Elbow method as well as the computation of Silhouette widths for a robustness check. Later, the properties of the groups are studied through several observable parameters as mentioned along with computed ones e.g. star formation rates ($SFRs$), virial masses, mass-spectra, dynamical time scales ($T_d$), etc. to get a deeper understanding of the star formation process and dynamical evolution of these clouds. It is found that cluster 1 is suitable for the formation of field stars, binary pairs, or stellar associations, whereas the clouds in cluster 2 and cluster 3 are favorable sites for the formation of Galactic clusters of moderate masses, and cluster 4 may produce massive Galactic clusters as well as a few globular clusters. Surprisingly, for each cluster, clouds at around Galacto-centric radius $\sim$8 kpc, and on the near Galactic plane has a significantly low $SFR$. These occurrences indicate that the star formation phenomenon has yet not started or the proneness to start in that region.