Scott Sommerfeldt Elected ASA President

Only three binaries have been identified among the Centaur population. Because their perihelia are significantly closer than those of other trans-Neptunian binaries (TNBs), these systems allow a detailed look at tight binaries in the broader trans-Neptunian object population and provide critical insight into the disruption of binaries as they enter the Centaur population. Using recent and archival Hubble Space Telescope observations, along with Keck data, we present a spin–orbit study of Typhon–Echidna. We find that the binary’s mutual orbit is inconsistent with a Keplerian orbit; more detailed non-Keplerian fits show that the mutual orbit is rapidly precessing. We measure Typhon’s dynamical oblateness, J2, at ∼10σ confidence and find that Typhon’s rotation pole is ≳20° misaligned with the binary’s mutual orbit. Assuming Typhon has a triaxial shape, our results, combined with rotational light curves and thermal measurements from the literature, suggest ellipsoidal semiaxes of  km,  km, and  km. We further investigate the observational consequences of the complex spin–orbit dynamics, including light-curve alteration by axial precession of Typhon and substantial changes to the system’s mutual event season. Based on the system’s dynamically excited state, we suggest a recent encounter with a giant planet may have substantially altered the system, potentially consistent with a binary in an early stage of disruption. This hypothesis can be tested with resolved photometric observations of the system. Our investigation highlights how non-Keplerian dynamics enhance our understanding of TNB systems and motivates ongoing observations of TNBs with astrometry, photometry, and stellar occultations.

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.