Rayleigh-Taylor Instability in a Relativistic Shock

Crowds at collegiate basketball games react acoustically to events on the court in many ways, including applauding, chanting, cheering, and making distracting noises. Acoustic features can be extracted from recordings of crowds at basketball games to train machine learning models to classify crowd reactions. Such models may help identify crowd mood, which could help players secure fair contracts, venues refine fan experience, and safety personnel improve emergency response services or to minimize conflict in policing. By exposing the key features in these models, feature selection highlights physical insights about crowd noise, reduces computational costs, and often improves model performance. Feature selection is performed using random forests and least absolute shrinkage and selection operator logistic regression to identify the most useful acoustic features for identifying and classifying crowd reactions. The importance of including short-term feature temporal histories in the feature vector is also evaluated. Features related to specific 1/3-octave band shapes, sound level, and tonality are highly relevant for classifying crowd reactions. Additionally, the inclusion of feature temporal histories can increase classifier accuracies by up to 12%. Interestingly, some features are better predictors of future crowd reactions than current reactions. Reduced feature sets are human-interpretable on a case-by-case basis for the crowd reactions they predict.

Particulate contamination requires dust mitigation techniques to provide low-scatter surfaces on sensitive instrumentation in space. We have shown that poly(olefin sulfone)s photodegrade under spacelike conditions: in vacuum and with UV light exposure. We now demonstrate that photodegradable polymers can reduce dust accumulation on optical surfaces for space applications. This investigation shows that the dissociative degradation of poly(olefin sulfone)s significantly decreased the number of dust particles on a dust-coated surface. These results suggest a powerful way to mitigate the collection of extraterrestrial dust on optical surfaces in space, enabling passive removal of particulate contamination without any direct human intervention.

Load modeling is a primary activity in deriving verifiable models of power systems. It is often argued that the uncertainty in load models exceeds that of other components by a wide margin. The problem is intrinsically challenging, as the acceptable solution consists of many heterogeneous and even disparate physical components. The number of parameters needed to describe a composite dynamic load captures one quantitative aspect of model simplification. This paper uses information geometry as the main tool in a two-step process–model simplification followed by parameter determination. The method offers global results in parameter estimation and quantifies the common challenges in fitting standard models to measurement data. We use a very detailed WECC composite load model embedded in the real world 441-bus benchmark system to illustrate the procedure and provide recommendations.

We present a combined magnetometry, muon spin-relaxation (𝜇⁢SR), and neutron-scattering study of the insulating spin glass Zn_0.5Mn_0.5Te, for which magnetic Mn²⁺ and nonmagnetic Zn2+ ions are randomly distributed on a face-centered cubic lattice. The magnetometry and 𝜇SR results confirm a spin freezing transition around 𝑇𝑓≈23 K, with the spin-fluctuation rate decreasing gradually and somewhat inhomogeneously through the sample volume as the temperature decreases toward 𝑇𝑓. Characteristic spin-correlation times well above 𝑇𝑓 are on the order of 10−10 s, much slower than typically observed in canonical spin glasses but in line with expectations for a cluster spin glass. Using magnetic pair distribution function (mPDF) analysis and reverse Monte Carlo (RMC) modeling of the magnetic diffuse neutron-scattering data, we show that the spin-glass ground state consists of clusters of spins exhibiting short-range-ordered type-III antiferromagnetic correlations with a locally ordered moment of 3.1(1)𝜇_B between nearest-neighbor spins. The type-III correlations decay exponentially as a function of spin separation distance with a correlation length of approximately 5 Å. The diffuse magnetic scattering and corresponding mPDF show no significant changes across 𝑇𝑓, indicating that the dynamically fluctuating short-range spin correlations in the paramagnetic state retain the same basic type-III configuration that characterizes the spin-glass state; the only change apparent from the neutron-scattering data is a gradual reduction of the correlation length and locally ordered moment with increasing temperature. Taken together, these results paint a unique and detailed picture of the local magnetic structure and dynamics in Zn_0.5Mn_0.5Te and provide strong evidence that this material is best described as a cluster spin glass. In addition, this work showcases a statistical method for extracting diffuse scattering signals from neutron powder diffraction data, which we developed to facilitate the mPDF and RMC analysis of the neutron data. This method has the potential to be broadly useful for neutron powder diffraction experiments on a variety of materials with short-range atomic or magnetic order.

Starshade technology represents a promising approach for direct exoplanet imaging, offering stellar light suppression up to a factor of 1010. Particulate contamination that clings to the edge of the starshade causes solar glint which could compromise Earth-like exoplanet detection. In previous research, when testing at atmospheric pressure, McKeithen et al. 2023 observed that the sharp edge have fewer particles larger than 14 microns, and more particles smaller than 14 microns, than expected from the surface distribution. To determine if this observation was reproducible in different environments, we contaminated Starshade edge coupons in low vacuum conditions. We characterized the surface and edge cleanliness level using optical microscopy, a custom ImageJ macro and R program that calculated the counts and area of particles in each image. We then compared our results with McKeithen et al. confirming their result, but the crossover occurred at particle diameters of about 3 microns rather than 14 microns. In our work, we propose different mechanisms which could affect any differences between the vacuum environment tests and the tests performed in air previously.

Xenon difluoride passivated aluminum with a lithium fluoride overcoat (Al+XeLiF) mirror coatings are promising candidates for future space telescope missions due to their high reflectance down to 100nm. The XeLiF mirror coating blocks aluminum oxidation. Aluminum oxide is undesirable since it significantly reduces the far UV (100-to-190nm) reflectance. Cleaning techniques for this ideal coating require the balance of traditional surface cleaning with careful handling of the hygroscopic LiF. Photonic Cleaning Technologies’ First Contact Polymers (FCP) are proven to clean and protect optical surfaces effectively. A specialized FCP formulation may be required for Al+XeLiF. We monitored Al+XeLiF changes under repeated application of four FCP formulations using variable angle spectroscopic ellipsometry (VASE), reflectance, and Atomic Force Microscopy (AFM). We also applied multiple FCP formulations to both Al+XeLiF and Al+LiF samples and stored them in different humidity environments to monitor potential protective qualities of each formulation. We observed that two FCP formulations can effectively protect Al+XeLiF in 40% RH without Al loss over half a year.