Radiation from Wavepackets

Mutual events of trans-Neptunian binaries (TNBs) provide rare opportunities to measure the physical and orbital properties of small bodies in the outer solar system. However, successful observations of these events have been limited by uncertain predictions. Here, we present probabilistic predictions of TNB mutual events occurring through the 2030s, using high-precision non-Keplerian orbit solutions from the Beyond Point Masses project combined with a Bayesian framework that propagates orbital and size uncertainties. Our methods generate distributions of event timing, duration, depth, and probability of occurrence, enabling direct assessment of observability. We provide predictions for five systems with ongoing or imminent mutual event seasons, including (38628) Huya, (58534) Logos–Zoe, (148780) Altjira, (469705) ǂKá̧gára-!Hãunu, and (524366) 2001 XR254. Preparing for upcoming events with long-baseline light-curve monitoring is vital, as events may be difficult to distinguish from a regular rotational light curve. Rapid dissemination of event detections will benefit the entire community, allowing predictions to be updated, ensuring that these rare mutual event opportunities can be fully exploited.

One approach to investigating parameter sensitivity in seabed models is to apply the techniques of information geometry. This paper provides an information geometric analysis of a sound propagation in a shallow-water waveguide, where the acoustic properties of the sediment are derived from the viscous grainshearing (VGS) model. Specifically, we consider single-frequency transmission loss (TL) across a wide range of VGS parameters. By exploring the limits and boundaries of the TL model manifolds, particularly as parameters approach both low and high extremes, this approach allows for the determination of relative stiffness and sloppiness of model parameters and provides indications of parameter hierarchies and correlations. Results include slices of the model manifold and matrices of information distances on a fivedimensional model man-ifold, representing the absolute transmission loss at 16 receiver depths for different sediment types. Careful examination of these results provides insights into the relative impact of VGS parameters and the delineation of limiting regions. This work demonstrates how information geometry can inform model selection and parametrization in geoacoustic inversion studies, leading to more efficient and interpretable models of the seabed.

We present a new two-dimensional (2D) map of total Galactic extinction, AV, across the entire dust half-layer from the Sun to extragalactic space for Galactic latitudes ∣b∣ > 13°, as well as a three-dimensional (3D) map of AV within 2 kpc of the Sun. These maps are based on AV and distance estimates derived from a data set, which utilizes Gaia Data Release 3 parallaxes and multi-band photometry for nearly 100 million dwarf stars. We apply our own corrections to account for significant systematics in this data set. Our 2D map achieves an angular resolution of 6

1, while the 3D map offers a transverse resolution of 3.56 pc—corresponding to variable angular resolution depending on distance—and a radial resolution of 50 pc. In constructing these maps, we pay particular attention to the solar neighborhood (within 200 pc) and to high Galactic latitudes. The 3D map predicts AV from the Sun to any extended object within the Galactic dust layer with an accuracy of σ(AV) = 0.1 mag. The 2D map provides AV estimates for the entire dust half-layer up to extragalactic distances with an accuracy of σ(AV) = 0.07 mag. We provide AV estimates from our maps for various classes of extended celestial objects with angular size primarily in the range of 2′–40′, including 19,809 galaxies and quasars, 170 Galactic globular clusters, 458 open clusters, and several hundred molecular clouds from two lists. We also present extinction values for 8293 Type Ia supernovae. Comparison of our extinction estimates with those from previous maps and literature sources reveals systematic differences, indicating large-scale spatial variations in the extinction law and suggesting that earlier 2D reddening maps based on infrared dust emission tend to underestimate low extinction values.

This paper introduces a neural network-based calibration for low-force sensors that operate in the sub-newton regime (0−1N) for wearable applications. The proposed calibration utilizes a fully-connected neural network to digitally reduce the sensor nonlinearity. The neural network is trained using data from a custom low-force measurement system with a novel compliant mechanism. Detailed study explores the tradeoffs between the neural network size and activation function with calibration accuracy. Measurement results demonstrate 4X improvement in the force sensor linearity, achieving errors less than 0.005 N. The proposed calibration is well-suited for wearable applications requiring precise low-force measurements.

The Small Body Dynamics Tool (SBDynT) is software written for the community of solar system small body researchers to perform dynamical classification, characterization, and investigation. SBDynT provides advanced simulation analysis capabilities that make it straightforward to determine mean-motion resonance occupation, proper orbital elements, and a variety of stability indicators. These calculations can be performed for small bodies that are known, newly discovered, or simulated; observational uncertainties can be incorporated through the use of dynamical clones. In this paper, we describe the methods for producing proper orbital elements and stability indicators, which serve as essential tools for characterizing dynamical stability and long-term evolution. Through extensive validation, we demonstrate that this code offers a robust open-source framework for investigating the dynamics of solar system small bodies with high accuracy. We also aim for computational efficiency allowing SBDynT to provide dynamical information for the several-fold increases in small bodies expected in the Legacy Survey of Space and Time era.

Machine learning is revolutionizing protein design by enabling the rapid generation of sequences with precise structural and functional properties. Controlling protein conformational states remains a major challenge, particularly for enzymes regulated by complex structural switches. Here, using high-resolution structural data and probabilistic sequence-structure models, a machine learning-driven framework for conformationally biased protein design is presented titled Conformation-Specific Design or CSDesign. This approach generates sequences predicted to favor a desired conformation while disfavoring alternative states. As a proof-of-concept, this approach is applied to extracellular signal-regulated kinase 2 (ERK2), generating variants predicted to favor the active or inactive state. Experimental validation of relative kinase activity in a controlled assay confirmed that an active-biased variant, CSD104, exhibits robust kinase activity without native upstream phosphorylation, while an inactive-biased variant, CSD101, remains inactivated. Structural analysis suggests that engineered interactions stabilize active-like features in place of phosphorylation. These results demonstrate machine learning control of protein conformational ensembles, with potential to design enzymes and other conformationally regulated proteins without relying on phosphomimetic mutations or extensive experimental screening.