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Abstract Most galaxies, including the Milky Way, host a supermassive black hole (SMBH) at the center. These SMBHs can be observed out to high redshifts (z≥ 6) if the accretion rate is sufficiently large. However, we do not fully understand the mechanism through which these black holes form at early times. The heavy (or direct collapse) seeding mechanism has emerged as a probable contender in which the core of an atomic cooling halo directly collapses into a dense stellar cluster that could host supermassive stars that proceed to form a black hole seed of mass ∼ 10⁵M_⊙. We use the Renaissance Simulations to investigate the properties of 35 direct collapse black hole (DCBH) candidate host halos atz = 15–24 and compare them to noncandidate halos. We aim to understand what features differentiate halos capable of hosting a DCBH from the general halo population with the use of statistical analysis and machine learning methods. We examine 18 halo, central, and environmental properties. We find that DCBH candidacy is more dependent on a halo’s core internal properties than on exterior factors such as Lyman–Werner (LW) flux and distance to the closest galaxy; our analysis selects density and radial mass influx as the most important features (outside candidacy establishing features). Our results concur with the recent suggestion that DCBH host halos neither need to lie within a “Goldilocks zone” nor have a significant amount of LW flux to suppress cooling. This paper presents insight to the dynamics possibly occurring in potential DCBH host halos and seeks to provide guidance to DCBH subgrid formation models. 

This project is focused on improving the optics in LIGO by characterizing mirror figure error that contribute to optical losses. We develop a method to measure surface deformations with in-Situ mode spectroscopy, measuring the resonant frequencies of the higher order Hermite Gaussian modes resonant in LIGO's Fabry-Perot cavities, that are shifted from their ideal spacings due to those deformations. We use this information to construct mirror phase maps. that characterize the figure error.