Total Solar Eclipse: August 21, 2017

At Brigham Young University, acoustic time reversal experiments are conducted in solids and fluid media. The experimental setup involves synchronized generation and acquisition hardware. The synchronized hardware allows for multi-channel generation, and in some cases, multi-channel acquisition of time reversal focusing. A LabVIEW application has been compiled to increase ease of use and repeatability for the students conducting experiments. Forward and backward steps of time reversal are conducted through this simple user interface. The software is also able to control a 2D positioning system that allows for the measurement of the spatial extent of a time reversal focus. Modifications to traditional time reversal processing, such as inverse filtering and one-bit processing, may be easily implemented in the software. This paper describes the hardware and software that facilitates time reversal research.

The electron-positron stage of the Future Circular Collider, FCC-ee, is a frontier factory for Higgs, top, electroweak, and flavour physics. It is designed to operate in a 100 km circular tunnel built at CERN, and will serve as the first step towards ≥100 TeV proton-proton collisions. In addition to an essential and unique Higgs program, it offers powerful opportunities to discover direct or indirect evidence of physics beyond the Standard Model. Direct searches for long-lived particles at FCC-ee could be particularly fertile in the high-luminosity Z run, where 5 × 10¹² Z bosons are anticipated to be produced for the configuration with two interaction points. The high statistics of Higgs bosons, W bosons and top quarks in very clean experimental conditions could offer additional opportunities at other collision energies. Three physics cases producing long-lived signatures at FCC-ee are highlighted and studied in this paper: heavy neutral leptons (HNLs), axion-like particles (ALPs), and exotic decays of the Higgs boson. These searches motivate out-of-the-box optimization of experimental conditions and analysis techniques, which could lead to improvements in other physics searches.

A downside of landing first-stage boosters of orbital-class launch vehicles, such as the Falcon 9, is the sonic boom associated with reentry and landing. To assess the potential impact of these sonic booms and compare them to launch and landing operations, acoustic data from a Falcon-9 launch and booster landing at Van-denberg Space Force Base are analyzed. The data were collected near Lompoc, CA, at a station 8 km away from the landing site. Because of the booster shape and landing orientation, the measured waveform contains three shocks (a triple boom), rather than the two associated with a traditional N-wave. Waveform and spectral characteristics are examined and various metrics, including A-weighted Sound Exposure Levels, are calculated and compared with those of the launch and landing noise.

Biological systems often have a narrow temperature range of operation, which require highly accurate spatially resolved temperature measurements, often near +/- 0.1 K. However, many temperature sensors cannot meet both accuracy and spatial distribution requirements, often because their accuracy is limited by data fitting and temperature reconstruction models. Machine learning algorithms have the potential to meet this need, but their usage in generating spatial distributions of temperature is severely lacking in the literature. This work presents the first instance of using neural networks to process fluorescent images to map the spatial distribution of temperature. Three standard network architectures were investigated using non-spatially resolved fluorescent thermometry (simply-connected feedforward network) or during image or pixel identification (U-net and convolutional neural network, CNN). Simulated fluorescent images based on experimental data were generated based on known temperature distributions where Gaussian white noise with a standard deviation of +/- 0.1 K was added. The poor results from these standard networks motivated the creation of what is termed a moving CNN, with an RMSE error of +/- 0.23 K, where the elements of the matrix represent the neighboring pixels. Finally, the performance of this MCNN is investigated when trained and applied to three distinctive temperature distributions characteristic within microfluidic devices, where the fluorescent image is simulated at either three or five different wavelengths. The results demonstrate that having a minimum of 10(3.5) data points per temperature and the broadest range of temperatures during training provides temperature predictions nearest to the true temperatures of the images, with a minimum RMSE of +/- 0.15 K. When compared to traditional curve-fitting techniques, this work demonstrates that greater accuracy when spatially mapping temperature from fluorescent images can be achieved when using convolutional neural networks.

The directional characteristics of human speech have many applications in speech acoustics, audio, telecommunications, room acoustical design, and other areas. However, professionals in these fields require carefully conducted, high-resolution, spherical speech directivity measurements taken under distinct circumstances to gain additional insights for their work. Because head orientation and human-body diffraction influence speech radiation, this work explores such effects under various controlled conditions through the changing directivity patterns of a head and torso simulator. The results show that head orientation and body diffraction at low frequencies impact directivities only slightly. However, the effects are more substantial at higher frequencies, particularly above 1 kHz.

We consider how mathematical models enable predictions for conditions that are qualitatively different from the training data. We propose techniques based on information topology to find models that can apply their learning in regimes for which there is no data. The first step is to use the manifold boundary approximation method to construct simple, reduced models of target phenomena in a data-driven way. We consider the set of all such reduced models and use the topological relationships among them to reason about model selection for new, unobserved phenomena. Given minimal models for several target behaviors, we introduce the supremum principle as a criterion for selecting a new, transferable model. The supremal model, i.e., the least upper bound, is the simplest model that reduces to each of the target behaviors. We illustrate how to discover supremal models with several examples; in each case, the supremal model unifies causal mechanisms to transfer successfully to new target domains. We use these examples to motivate a general algorithm that has formal connections to theories of analogical reasoning in cognitive psychology.