Scott Sommerfeldt Elected ASA President

We present new spectroscopic observations of the inner circumgalactic medium (CGM) of NGC 891 taken with the Mid-Infrared Imager/Medium Resolution Spectroscopy instrument on board JWST, in four positions: two near the bulge and two at galactocentric radii (r) of ∼1.5, 4.7 kpc. Each pair of pointings has one position along the minor axis (h) at ∼0.5 kpc and one at ∼1 kpc away from the mid-plane. We analysed 1D spectra and 3D cubes using the dust emission model PAHFIT to extract properties of typical mid-IR features. These spectra reveal that the earlier reported mid-IR emission out to 4 kpc is dominated by the emission of polycyclic aromatic hydrocarbons (PAHs), and not hot dust continuum, thus providing direct evidence of the survival of PAHs in the inner CGM of NGC 891. Comparing PAH band ratios with other environments (Orion, M51), it is obvious that the 11.2 μm PAH feature – and not the usual 7.7 μm – dominates in NGC 891, which seems to imply the presence of more neutral, large PAHs in the CGM. Overall, PAH-to-continuum ratios show little variations with scale-height and radius in NGC 891, which suggests little PAH processing. However, we do see a decrease in the PAH feature strengths with the [Ne III]/[Ne II] ratio, which points to elevated dust processing with increased radiation field hardness. We also confirm a tight correlation between H2 and PAH features, which suggests that the two tracers must be co-spatial, and hence implies that PAH emission predominantly arises from cool dense parts of cloudlets entrained in galactic outflows. Finally, we report the clear detection of a previously unidentified PAH feature at 16.72 μm.

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.