First Light for the BYU 0.9-m Telescope

To improve acoustical models of super heavy-lift launch vehicles, this Letter reports Space Launch System's (SLS's) overall sound power level (OAPWL) and compares it to NASA's past lunar rocket, the Saturn V. Measurements made 1.4–1.8 km from the launchpad indicate that SLS produced an OAPWL of 202.4 (⁠ 

0.5) dB re 1 pW and acoustic efficiency of about 0.33%. Adjustment of a static-fire sound power spectrum for launch conditions implies Saturn V was at least 2 dB louder than SLS with approximately twice the acoustic efficiency.

Polar-ring galaxies (PRGs) are an outstanding example of galaxies with misaligned kinematics where a typically red central galaxy is surrounded by a large-scale ring or disc of stars, gas, and dust oriented almost perpendicular to the main body. It is believed that polar structures (PSs) are formed in a secondary event after the assembly of a central galaxy, but due to their scarcity, their formation paths are not well constrained yet. We present a study of PRGs from TNG50 cosmological simulations, focusing on the origin of their PSs. Based on the synthetic images and baryonic mass distribution, we found six galaxies with stellar polar rings. Using Supplementary Data Catalogues and available particle data, we confirm that the selected galaxies are direct analogues of real PRGs. In our sample, the PSs are a result of the close interaction between the host galaxy and its companion. We track two formation paths for the stellar polar rings in our sample: (i) star formation in the accreted gas and (ii) tidal disruption of the satellite's stellar component. Rings formed during the first scenario are, on average, bluer and younger than ones formed due to the satellite disruption. We report a steady increase of the ring's inclination around the two most massive galaxies across a few billion years with a rate of approximate to 8(degrees) Gyr(-1). The formation of a PS in some cases can increase the nuclear activity of the central galaxy and/or turn the active nucleus off completely.

The National Transportation Noise Map (NTNM) gives time-averaged traffic noise across the continental United States (CONUS) using annual average daily traffic. However, traffic noise varies significantly with time. This paper outlines the development and utility of a traffic volume model which is part of VROOM, the Vehicular Reduced-Order Observation-based model, which, using hourly traffic volume data from thousands of traffic monitoring stations across CONUS, predicts nationwide hourly varying traffic source noise. Fourier analysis finds daily, weekly, and yearly temporal traffic volume cycles at individual traffic monitoring stations. Then, principal component analysis uses denoised Fourier spectra to find the most widespread cyclic traffic patterns. VROOM uses nine principal components to represent hourly traffic characteristics for any location, encapsulating daily, weekly, and yearly variation. The principal component coefficients are predicted across CONUS using location-specific features. Expected traffic volume model sound level errors—obtained by comparing predicted traffic counts to measured traffic counts—and expected NTNM-like errors, are presented. VROOM errors are typically within a couple of decibels, whereas NTNM-like errors are often inaccurate, even exceeding 10 decibels. This work details the first steps towards creation of a temporally and spectrally variable national transportation noise map.

The acoustic black hole (ABH) effect has been shown to increase damping of structures by focusing energy into a tapered-thickness region with added damping material. This paper illustrates that enhanced damping can be achieved without the use of damping material. Three panels were designed with different ABH grid patterns and parameters and compared to a baseline panel. Increased damping is shown to exist for two of the three ABH panels even though no damping material was applied. The panel modes which exhibited increased damping were local to the ABH grid while global modes did not exhibit increased damping.

Superthin galaxies are bulgeless low-surface brightness galaxies with unusually high major-to-minor axes ratio of the stellar disc, i.e. 10 < a/b < 20. We present Giant Metrewave Radio Telescope (GMRT) Hi 21cm radio-synthesis observations of FGC 2366, the thinnest galaxy known with a/b = 21.6. Employing the 3D tilted-ring modelling using fully automated TiRiFiC (fat), we determine the structure and kinematics of the Hi gas disc, obtaining an asymptotic rotational velocity equal to 100 kms(-1) and a total Hi mass equal to 10(9)M(circle dot). Using z-band stellar photometry, we obtain a central surface brightness of 22.8 mag arcsec(-2), a disc scale length of 2.6 kpc, and a scale height of 260 pc. Next, we determine the dark matter density profile by constructing a mass model and find that an Navarro-Frenk-White (NFW) dark matter halo best-fits the steeply rising rotation curve. With the above mass inventory in place, we finally construct the dynamical model of the stellar disc of FGC 2366 using the stellar dynamical code 'agama'. To identify the key physical mechanisms responsible for the superthin vertical structure, we carry out a Principal Component Analysis of the data corresponding to all the relevant dynamical parameters and a/b for a sample of superthin and extremely thin galaxies studied so far. We note that the first two principal components explain 80 percent of the variation in the data, and the significant contribution is from the compactness of the mass distribution, which is fundamentally responsible for the existence of superthin stellar discs.

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density functional theory calculations that have the same cell size as the simulations that the potential is explicitly trained to model. Here, we demonstrate an easy-to-implement small-cell training protocol and use it to model the Zr-H system. This training leads to a potential that accurately predicts known stable Zr-H phases and reproduces the α-β pure zirconium phase transition in molecular dynamics simulations. Compared to traditional active learning, small-cell training decreased the training time of the α-β zirconium phase transition by approximately 20 times. The potential describes the phase transition with a degree of accuracy similar to that of the large-cell training method.