First Light for the BYU 0.9-m Telescope

Nonlinear propagation of noise from military jet aircraft has been fairly well documented, but only within a few hundred meters from the aircraft. This paper describes analysis of nonlinear propagation for morning static runups of F-35 aircraft at greater distances, out to 1220 m near the direction of maximum radiation and at heights ranging from 0 m up to 30.5 m. A comparison of overall levels with distance and height reveals evidence of significant atmospheric refraction effects, and a general trend of decreasing level with height. Examination of nonlinearity metrics reveals opposite behavior, however. At these distances, nonlinear propagation effects are often strongest in waveforms with lower sound levels, which is counterintuitive. One important finding, however, is that acoustic shock strength can vary greatly from runup to runup, even for seemingly small changes in atmospheric conditions. This analysis demonstrates the need for further research into long-range nonlinear propagation of jet noise through realistic atmospheric conditions.

We have applied machine learning in a neural network to calculate the quasi TE₀₁₁ mode of a cylindrical microwave cavity with two symmetrically stacked dielectric resonators (DRs) inside, with aspect ratios of the overall cavity being limited to the range of 0.25-4. The neural network was trained with 99 970 samples and evaluated using 9564 samples from a holdout dataset. The samples were created using a supercomputer to solve random cavity configurations via finite-element method (FEM) programming. The trained neural network predicts the resonant frequency of the quasi TE₀₁₁ mode and expresses the mode in terms of expansion coefficients of empty cavity TE 0,np modes, from which plots of the electric and magnetic fields can be made. The predictions are extremely quick, taking ~0.05-0.2 s running on a typical personal computer, and are very accurate when judged against the FEM results: the overall median error in the frequency neural network is 0.2%, and the overall median error of the expansion coefficients neural network is 0.003%. This should allow designers to much more rapidly determine optimal cavity and DR dimensions and other parameters in order to achieve the frequency and mode they desire, with a speedup of approximately 10 000x compared with FEM calculations alone. A link to the Python implementation of our FEM code and our trained neural network code is provided.

Wind-induced microphone self-noise is a non-acoustic signal that may contaminate outdoor acoustical measurements, particularly at low frequencies, even when using a windscreen. A recently developed method [Cook et al., JASA Express Lett. 1, 063602 (2021)] uses the characteristic spectral slope of wind noise in the inertial subrange for screened microphones to automatically classify and reduce wind noise in acoustical measurements in the lower to middling frequency range of human hearing. To explore its uses and limitations, this method is applied to acoustical measurements which include both natural and anthropogenic noise sources. The method can be applied to one-third octave band spectral data with different frequency ranges and sampling intervals. By removing the shorter timescale data at frequencies where wind noise dominates the signal, the longer timescale acoustical environment can be more accurately represented. While considerations should be made about the specific applicability of the method to particular datasets, the wind reduction method allows for simple classification and reduction of wind-noise-contaminated data in large, diverse datasets.