Black Hole Growth in Disk Galaxies Mediated by the Secular Evolution of Short Bars

The growth of black holes (BHs) in disk galaxies lacking classical bulges, which implies an absence of significant mergers, appears to be driven by secular processes. Short bars of sub-kiloparsec radius have been hypothesized to be an important mechanism for driving gas inflows to small scale, feeding central BHs. In order to quantify the maximum BH mass allowed by this mechanism, we examine the robustness of short bars to the dynamical influence of BHs. Large-scale bars are expected to be robust, long-lived structures; extremely massive BHs, which are rare, are needed to completely destroy such bars. However, we find that short bars, which are generally embedded in largescale outer bars, can be destroyed quickly when BHs of mass Mbh ∼ 0.05% 0.2% of the total stellar mass (M∗) are present. In agreement with this prediction, all galaxies observed to host short bars have BHs with a mass fraction less than 0.2% M∗. Thus, the dissolution of short inner bars is possible, perhaps even frequent, in the universe. An important implication of this result is that inner-bar-driven gas inflows may be terminated when BHs grow to ∼0.1% M∗. We predict that 0.2% M∗ is the maximum mass of BHs allowed if they are fed predominately via inner bars. This value matches well the maximum ratio of BH-to-host-galaxy stellar mass observed in galaxies with pseudo-bulges and most nearby active galactic nucleus host galaxies. This hypothesis provides a novel explanation for the lower M Mbh in galaxies that have avoided significant mergers compared with galaxies with classical bulges

Identifying Kinematic Structures in Simulated Galaxies Using Unsupervised Machine Learning

Galaxies host a wide array of internal stellar components, which need to be decomposed accurately in order to understand their formation and evolution. While significant progress has been made with recent integral-field spectroscopic surveys of nearby galaxies, much can be learned from analyzing the large sets of realistic galaxies now available through state-of-the-art hydrodynamical cosmological simulations. We present an unsupervised machine-learning algorithm, named auto-GMM, based on Gaussian mixture models, to isolate intrinsic structures in simulated galaxies based on their kinematic phase space. For each galaxy, the number of Gaussian components allowed by the data is determined through a modified Bayesian information criterion. We test our method by applying it to prototype galaxies selected from the cosmological simulation IllustrisTNG. Our method can effectively decompose most galactic structures. The intrinsic structures of simulated galaxies can be inferred statistically by non-human supervised identification of galaxy structures. We successfully identify four kinds of intrinsic structures: cold disks, warm disks, bulges, and halos. Our method fails for barred galaxies because of the complex kinematics of particles moving on bar orbits