Hierarchical ammonia structures in galactic molecular clouds




Keown, Jared

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Recent large-scale mapping of dust continuum emission from star-forming clouds has revealed their hierarchical nature, which includes web-like filamentary structures that often harbor clumpy over-densities where new stars form. Understanding the motions of these structures and how they interact to form stars, however, can only be learned through observations of emission from their molecular gas. Observations of tracers such as ammonia (NH3), in particular, reveal the stability of dense gas structures against forces such as the inward pull of gravity and the outward push of their internal pressure, thus providing insights into whether or not those structures are likely to form stars in the future. Due to recent large-scale ammonia surveys that have mapped both nearby and distant clouds in the Galaxy, it is finally possible to investigate and compare the stability of star-forming structures in different environments. In this dissertation, we utilize ammonia survey data to provide one of the largest investigations to date into the stability of structures in star-forming regions. Dense gas structures have been identified in a self-consistent manner across a variety of star-forming regions and the environmental factors (e.g., the presence or lack of local filaments and heating by local massive stars) most influential to their stability were investigated. The analysis has revealed that dense gas structures identified by ammonia observations in nearby star-forming clouds tend to be gravitationally bound. In high-mass star-forming clouds, however, bound and unbound ammonia structures are equally likely. This result suggests that either gravity is more important to structure stability at the small scales probed in nearby clouds or ammonia is more widespread in high-mass star-forming regions. In addition, a new method to detect and measure emission with multiple velocity components along the line of sight has been developed. Based on convolutional neural networks and named Convnet Line-fitting Of Emission-line Regions (CLOVER), the method is markedly faster than traditional analysis techniques, requires no input assumptions about the emission, and has demonstrated high classification accuracy. Since high-mass star-forming regions are often plagued by multiple velocity components along the line of sight, CLOVER will improve the accuracy of stability measurements for many clouds of interest to the star formation community.



interstellar medium, star formation, convolutional neural networks, astronomy, astrophysics, molecular clouds, young stellar objects, stars, ammonia, virial stability, machine learning