Scott Sommerfeldt Elected ASA President

When a sonic boom propagates through the atmospheric boundary layer, atmospheric turbulence distorts the waveform, producing stochastic variations over short distances. Accurate prediction of this variability is essential because turbulence affects both waveform characteristics and human-perception metrics used to evaluate supersonic overflights. During NASA's 2019 Carpet Determination in Entirety Measurements I (CarpetDIEM I) flight test campaign, Brigham Young University deployed a 120 m (400 ft) linear array with seven microphones to quantify turbulence-induced variability of sonic boom waveforms, spectra, and metrics. In this study, state-of-the-art models PCBoom and KZKFourier are combined with two weather models, CFSv2 and ERA5, to predict metric variability and compare results with CarpetDIEM I measurements. Depending on the metric and confidence interval considered, prediction success ranges between about 30% and 80%. For the perceived level metric, the prediction successes for the mean and standard deviation were 30%–48% and 52%–65% respectively, depending on the weather model. This compares favorably with the Sonic Booms in Atmospheric Turbulence prediction success results of 45% and 71% for the perceived level mean and standard deviation respectively, using data reported by Stout, Sparrow, and Blanc-Benon [(2021). J. Acoust. Soc. Am. 149, 3250–3260].

Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful tools for large-scale materials modeling. The accuracy of MLIPs is typically validated on a held-out dataset of ab initio energies and atomic forces. However, accuracy on these small-scale properties does not guarantee reliability for emergent, system-level behavior—precisely the regime where atomistic simulations are most needed, but for which direct validation is often computationally prohibitive. As a practical heuristic, predictive precision—quantified as inverse uncertainty—is commonly used as a proxy for accuracy, but its reliability remains poorly understood, particularly for system-level predictions. In this work, we systematically assess the relationship between predictive precision and accuracy in both in-distribution (ID) and out-of-distribution (OOD) regimes, focusing on ensemble-based uncertainty quantification (UQ) methods for neural network potentials, including bootstrap, dropout, random initialization, and snapshot ensembles. We use held-out cross-validation for ID assessment and calculate cold curve energies and phonon dispersion relations for OOD testing. These evaluations are performed across various carbon allotropes as representative test systems. We find that uncertainty estimates can behave counterintuitively in OOD settings, often plateauing or even decreasing as predictive errors grow. These results highlight fundamental limitations of current UQ approaches and underscore the need for caution when using predictive precision as a stand-in for accuracy in large-scale, extrapolative applications.

Machine-learning models that estimate crowd engagement at college football games could improve the home-field advantage, help stadiums serve fans better, and become a tool for other scientific studies. To prepare to create these models, college football games were acoustically characterized with feature identification and selection in mind. Data were collected from six Brigham Young University (BYU) football games and analyzed for patterns in level, spectra, and principal components. The probability distribution of sound pressure levels from an entire game was found to contain three distinct peaks that were found to be associated with (1) quiet during offense, (2) music being played over the stadium loudspeakers, and (3) distraction noise when BYU was on defense. Crowd-generated sounds were found to have the most energy in the 630 and 800 Hz bands, consistent with other crowd noise previously studied. Furthermore, a principal component analysis on the football games’ one-third octave spectra revealed that the principal components were similar to those from BYU basketball and volleyball games. Based on these acoustic characteristics, the levels of the 100 Hz, 800 Hz, and 2.5 kHz bands are proposed as features for estimating crowd engagement at college football games.

We present Atacama Large Millimeter/submillimeter Array (ALMA) Cycle 7 12CO(2−1) observations of the circumnuclear disk in NGC 315 at an angular resolution of , improving on past measurements and resolving the sphere of influence (SOI) of the supermassive black hole (SMBH), whose mass has been estimated previously as MBH  M⊙. The high spatial resolution and sensitivity enable robust full-cube forward modeling of the molecular gas kinematics and a direct comparison of multiple independent gas-based dynamical modeling techniques. We apply standard Bayesian codes using both Markov chain Monte Carlo (MCMC) and nested sampling approaches, as well as a frequentist code to the same data set, exploring systematic uncertainties associated with the stellar mass distribution, gas surface-brightness parameterization, and disk geometry. All methods yield consistent black hole masses, indicating that the inferred MBH is not strongly method-dependent. Combining the ensemble of independent molecular-gas-based models, we derive an ensemble median black hole mass of (stat)(sys), where the comparable contributions to the full error budget arise from modeling systematics rather than formal fitting uncertainties. Our MBH is consistent with the empirical MBH–σ⋆ and MBH–Lbulge scaling relations, and lies 32% below an independent stellar-dynamical measurement—a discrepancy we discuss in the context of systematic differences between gas- and stellar-based methods. NGC 315 serves as a benchmark for quantifying molecular gas-dynamical MBH systematic uncertainties and for future cross-comparisons of gaseous and stellar dynamical approaches.

We investigate how local magnetic correlations evolve when the classical pyrochlore spin ice Ho₂Ti₂O₇ is transformed into the partially disordered stuffed compound Ho₂TiO₅. Neutron scattering measurements were analyzed using real space magnetic pair distribution function, reciprocal space reverse Monte Carlo, and half-polarized neutron powder diffraction methods to connect the average crystal structure with local magnetic correlations. Both compounds retain long-range 𝐹𝑑3𝑚 symmetry, but in Ho₂TiO₅ the average structure does not fully capture the local Ho–O, Ti–O, and O–O bond-length disorder associated with partial Ho/Ti occupancy. Despite this disorder, half-polarized neutron powder diffraction shows that the Ho moments retain local ⟨111⟩ Ising anisotropy and form spin-in/spin-out configurations on the tetrahedral network. In Ho₂Ti₂O₇, real- and reciprocal-space analyses show a nearly ideal 2-in/2-out spin ice state at 0.3 K, with ∼95% of tetrahedra satisfying the ice rule, followed by progressive thermal disordering on warming. In Ho₂TiO₅, most tetrahedra are magnetically incomplete because some tetrahedral vertices are occupied by nonmagnetic Ti rather than Ho; nevertheless, the dominant incomplete configurations are 2-in/1-out and 1-in/2-out, which are locally compatible with the ice rule if the missing Ho spin is restored. Ho₂TiO₅ is therefore a vacancy-disrupted spin ice in which the local ⟨111⟩ Ising character and short-range spin-in/spin-out correlations persist on a topologically incomplete magnetic network, while collective ice-rule correlations are strongly disrupted.