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Abstract The streaming instability (SI) is a leading candidate for planetesimal formation, which can concentrate solids through two-way aerodynamic interactions with the gas. The resulting concentrations can become sufficiently dense to collapse under particle self-gravity, forming planetesimals. Previous studies have carried out large parameter surveys to establish the critical particle to gas surface density ratio (Z), above which SI-induced concentration triggers planetesimal formation. The thresholdZdepends on the dimensionless stopping time (τ_s, a proxy for dust size). However, these studies neglected both particle self-gravity and external turbulence. Here, we perform 3D stratified shearing box simulations with both particle self-gravity and turbulent forcing, which we characterize via a turbulent diffusion parameter,α_D. We find that forced turbulence, at amplitudes plausibly present in some protoplanetary disks, can increase the thresholdZby up to an order of magnitude. For example, forτ_s= 0.01, planetesimal formation occurs whenZ≳ 0.06, ≳0.1, and ≳0.2 atα_D= 10⁻⁴, 10^−3.5, and 10⁻³, respectively. We provide a single fit to the criticalZrequired for the SI to work as a function ofα_Dandτ_s(although limited to the rangeτ_s= 0.01–0.1). Our simulations also show that planetesimal formation requires a mid-plane particle-to-gas density ratio that exceeds unity, with the critical value being largely insensitive toα_D. Finally, we provide an estimation of particle scale height that accounts for both particle feedback and external turbulence. 

The neutral hydrogen (HI) in galaxies provides the gas reservoir out of which stars are formed. The ability to determine the HI masses for statistically significant samples of galaxies can provide information about the connection between this gas reservoir and the star formation that drives galaxy evolution. However, there are relatively few galaxies for which HI masses are known because these measurements are significantly more difficult to make than optical observations. Artificial neural networks are a type of nonlinear technique that have been used estimate the gas masses from their optical properties (Teimoorinia et al. 2017). We present HI observations of 51 galaxies with gas and stellar properties that are rare in the Arecibo Legacy Fast ALFA Survey (ALFALFA, Haynes et al. 2018) which was used to train the Artificial Neural Network developed by Teimoorinia et al. (ANN, 2017). These sources provide a test of the Artificial Neural Network predictions of HI mass and include some rare and interesting systems including galaxies that are extremely massive in both stellar mass (log M_∗> 11.0) and HI mass (log M_HI> 10.2) with large HI line widths (w_50> 500 km/s). We find that this Artificial Neural Network systematically overestimates the gas fraction of the galaxies in our selected sample, suggesting that care must be taken when using these techniques to predict gas masses for galaxies from a broad range of optical properties.