Gaseous Halos of Simulated Milky Way-like Galaxies

In order to investigate the role of the host halo in quenching satellite galaxies, we have characterized a single Milky Way-like host galaxy from the FIRE simulations from z = 0–1.76 by quantifying the gas density of the host halo environment with respect to distance from the host and galactocentric latitude. The gas density decreases with increasing distance from the host according to a broken power law. At earlier times (2–10 Gyr ago), the density in the inner regions of the host halo was enhanced relative to z = 0. Thus, earlier infalling satellites experienced more ram-pressure and were more efficiently quenched compared to later infalling satellites. We also find that in the inner halo (<150 kpc) the density is 2–3 times larger close to the plane of the host galaxy disk versus above or below the disk, so satellites that orbit at low galactocentric latitudes may be more efficiently quenched.

Deterministic Modeling to Predict the Natural Gas Density Using Artificial Neural Networks

During the past decades, several research studies have been made to unfold the immense and diversified benefits of the innovative applications of machine learning (ML) techniques in the petroleum industry. For instance, machine learning algorithms were applied to estimate the various physical properties of natural gas. Natural gas density is considered an indispensable metric that influences the determination of several variables necessary for analyzing natural gas systems. In this work, the Artificial neural network (ANN), a machine learning technique, was applied to estimate natural gas density incorporating the influencing factors. The ANN model was also compared with another ML technique, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS). A mathematical form has been also presented using ANN. A real data set was taken from the literature, comprised of about 4500 data points assimilating three influencing input variables, including pseudo-reduced pressure (PPr), pseudo-reduced temperature (TPr), and molecular weight (Mw). The PPr and TPr are obtained by calculating the averages of the sample gas critical pressures and critical temperatures. A complicated nonlinear relationship exists between the three influencing variables and the gas density. The data set was divided into a 70:30 ratio for training and testing the model, respectively. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were applied to train and test the model. Absolute average percentage error (AAPE), coefficient of determination (R2), and root mean squared error (RMSE) were considered in the error metrics to acquire the best possible model. Levenberg–Marquardt backpropagation algorithm was employed for ANN, while subtractive clustering was used for ANFIS. Results showed that natural gas density can be well correlated with numerous inputs using machine learning tools (ANN and ANFIS). The input parameters include Ppr, Tpr, and Mw, as mentioned above. ANN performed better than ANFIS. The network was adjusted against the training sub-set to set-up weights and biases covering each node. R2 for both testing and training data was more than 99%, while AAPE was around 4% for both cases. Moreover, a detailed mathematical scheme for the ANN model is also provided in this paper.

Anomalous Hydrogen Recombination Line Ratios in Ultraluminous Infrared Galaxies

We conducted systematic observations of the H i Brα (4.05 μm) and Brβ (2.63 μm) lines in 52 nearby (z < 0.3) ultraluminous infrared galaxies (ULIRGs) with AKARI. Among 33 ULIRGs wherein the lines are detected, 3 galaxies show anomalous Brβ/Brα line ratios (∼1.0), which are significantly higher than those for case B (0.565). Our observations also show that ULIRGs have a tendency to exhibit higher Brβ/Brα line ratios than those observed in Galactic H ii regions. The high Brβ/Brα line ratios cannot be explained by a combination of dust extinction and case B since dust extinction reduces the ratio. We explore possible causes for the high Brβ/Brα line ratios and show that the observed ratios can be explained by a combination of an optically thick Brα line and an optically thin Brβ line. We simulated the H ii regions in ULIRGs with the Cloudy code, and our results show that the high Brβ/Brα line ratios can be explained by high-density conditions, wherein the Brα line becomes optically thick. To achieve a column density large enough to make the Brα line optically thick within a single H ii region, the gas density must be as high as n ∼ 108 cm−3. We therefore propose an ensemble of H ii regions, in each of which the Brα line is optically thick, to explain the high Brβ/Brα line ratio.

Oscillation of Gas Density in the Gas Filament Remained by a Streamer Discharge in Water

When a streamer discharge occurs in water, several luminous plasma filaments will be created in the water during the discharge. After the discharge, these plasma filaments turn into neutral gas phase and remain in water. The gas filament remained in water is a good object for studying the basic processes involved in the streamer propagation. We investigated the evolution of the gas filaments remained in water after a streamer discharge at different experimental conditions. We recorded eight successive images during one discharge pulse. The density of gas in the gas filament and the radius of the gas filament were measured from the obtained images. We found that the radius of the gas filament and the density of gas in the gas filament are almost not influenced by the impulse voltage within the range studied. While the conductivity of water has strong effect on the radius of the gas filament and the density of gas in the gas filament. The radius of the gas filament becomes thicker and expands faster as the conductivity of water becomes larger. The density of gas in the gas filament remained in water oscillates between 400 to 800 kg/m3 with an duration of ~10 μs during the expansion period of 4–39 μs after the HV pulse starts. Both the impulse voltage and the conductivity of water do not affect the oscillation duration of the density of gas in the gas filament.