Tendency of Adhesive Particles on the Liquid Wall Layer in the Turbulent Flow Channel

The experimental investigation and simulation model approach were carried out to investigate the behavior of the fine particles to ad­here on the layer of liquid on the wall in gas-solid two-phase flow. Polymethyl methacrylate having two different mean-diameters of 20 mm and of 50mm was used for measurement. By using continuous feeding sys­tem, the fine particles were entrained and mixed with the air in the duct. Experiment for solid particle gas with two-phase flow in room temperature was carried out to make a clear turbulent effect for particle adhering behavior to wall side having a high-viscosity liquid layer. These phenomena were also investigated by the simulation model which represented the experimental condition for two-phase flow and using k-ε two equation models for turbulent flow. The experimental result showed that adhered particle quantity depends on particle feeding rate. The result of simulation model also showed the same tendency. The relation of the various particles feeding rate and capture rate were also described

SILVERRUSH X:Machine Learning-aided Selection of 9318 LAEs at z=2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS Survey Data

We present a new catalog of $9318$ Ly$\alpha$ emitter (LAE) candidates at $z = 2.2$, $3.3$, $4.9$, $5.7$, $6.6$, and $7.0$ that are photometrically selected by the SILVERRUSH program with a machine learning technique from large area (up to $25.0$ deg$^2$) imaging data with six narrowband filters taken by the Subaru Strategic Program with Hyper Suprime-Cam (HSC SSP) and a Subaru intensive program, Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS). We construct a convolutional neural network that distinguishes between real LAEs and contaminants with a completeness of $94$% and a contamination rate of $1$%, enabling us to efficiently remove contaminants from the photometrically selected LAE candidates. We confirm that our LAE catalogs include $177$ LAEs that have been spectroscopically identified in our SILVERRUSH programs and previous studies, ensuring the validity of our machine learning selection. In addition, we find that the object-matching rates between our LAE catalogs and our previous results are $\simeq 80$-$100$% at bright NB magnitudes of $\lesssim 24$ mag. We also confirm that the surface number densities of our LAE candidates are consistent with previous results. Our LAE catalogs will be made public on our project webpage.Comment: 19 pages, 10 figures, accepted for publication in ApJ. Our LAE catalogs will become available at http://cos.icrr.u-tokyo.ac.jp/rush.htm