Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a transformer-based model that forecasts densities up to three days ahead and is intended as a drop-in replacement for an empirical baseline. Unlike recent approaches, it avoids spatial reduction and complex input pipelines, operating directly on a compact input set. Validated on real-world data, the model improves key prediction metrics and shows potential to support mission planning.

The precise insertion of CubeSats into designated orbits is a complex task, primarily due to the limited propulsion capabilities and constrained fuel reserves onboard, which severely restrict the scope for large orbital corrections. This limitation necessitates the development of more efficient maneuvering techniques to ensure mission success. In this paper, we propose a maneuvering sequence that exploits the natural J2 perturbation caused by the Earth's oblateness. By utilizing the secular effects of this perturbation, it is possible to passively influence key orbital parameters such as the argument of perigee and the right ascension of the ascending node, thereby reducing the need for extensive propulsion-based corrections. The approach is designed to optimize the CubeSat's orbital insertion and minimize the total fuel required for trajectory adjustments, making it particularly suitable for fuel-constrained missions. The proposed methodology is validated through comprehensive numerical simulations that examine different initial orbital conditions and perturbation environments. Case studies are presented to demonstrate the effectiveness of the J2-augmented strategy in achieving accurate orbital insertion, showing a major reduction in fuel consumption compared to traditional methods. The results underscore the potential of this approach to extend the operational life and capabilities of CubeSats, offering a viable solution for future low-Earth orbit missions.

With the rapid advancement of aerospace technology and the large-scale deployment of low Earth orbit (LEO) satellite constellations, the challenges facing astronomical observations and deep space exploration have become increasingly pronounced. As a result, the demand for high-precision orbital data on space objects-along with comprehensive analyses of satellite positioning, constellation configurations, and deep space satellite dynamics-has grown more urgent. However, there remains a notable lack of publicly accessible, real-world datasets to support research in areas such as space object maneuver behavior prediction and collision risk assessment. This study seeks to address this gap by collecting and curating a representative dataset of maneuvering behavior from Starlink satellites. The dataset integrates Two-Line Element (TLE) catalog data with corresponding high-precision ephemeris data, thereby enabling a more realistic and multidimensional modeling of space object behavior. It provides valuable insights into practical deployment of maneuver detection methods and the evaluation of collision risks in increasingly congested orbital environments.

Constraining planet formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant impact phase, during which planetary embryos evolve gravitationally and combine to form planets, which may themselves experience later collisions. To accelerate giant impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than 500,000 $N$-body simulations of three-planet systems, we develop an ML model that can accurately predict which two planets will experience a collision, along with the state of the post-collision planets, from a short integration of the system's initial conditions. Our model greatly improves on non-ML baselines that rely on metrics from dynamics theory, which struggle to accurately predict which pair of planets will experience a collision. By combining with a model for predicting long-term stability, we create an efficient ML-based giant impact emulator, which can predict the outcomes of giant impact simulations with a speedup of up to four orders of magnitude. We expect our model to enable analyses that would not otherwise be computationally feasible. As such, we release our full training code, along with an easy-to-use API for our collision outcome model and giant impact emulator.

Asteroid diameters are traditionally difficult to estimate. When a direct measurement of the diameter cannot be made through either occultation or direct radar observation, the most common method is to approximate the diameter from infrared observations. Once the diameter is known, a comparison with visible light observations can be used to find the visible geometric albedo of the body. One of the largest datasets of asteroid albedos comes from the NEOWISE mission, which measured asteroid albedos both in the visible and infrared. We model these albedos as a function of proper elements available from the Asteroid Families Portal using an ensemble of neural networks. We find that both the visible and infrared geometric albedos are significantly correlated with asteroid position in the belt and occur in both asteroid families and in the background belt. We find that the ensemble's prediction reduces the average error in albedo by about 37% compared to a model that simply adopts an average albedo, with no regard for the dynamical state of the body. We then use this model to predict albedos for the half million main belt asteroids with proper elements available in the Asteroid Families Portal and provide the results in a catalog. Finally, we show that several presently categorized asteroid families exist within much larger groups of asteroids of similar albedos - this may suggest that further improvements in family identification can be made.

A Measurement of the Kuiper Belt's Mean Plane From Objects Classified By Machine Learning Submitted to AJ ABSTRACT Mean plane measurements of the Kuiper Belt from observational data are of interest for their potential to test dynamical models of the solar system. Recent measurements have yielded inconsistent results. Here we report a measurement of the Kuiper Belt's mean plane with a sample size more than twice as large as in previous measurements. The sample of interest is the non-resonant Kuiper belt objects, which we identify by using machine learning on the observed Kuiper Belt population whose orbits are well-determined. We estimate the measurement error with a Monte Carlo procedure. We find that the overall mean plane of the non-resonant Kuiper Belt (semimajor axis range 35-150 au) and also that of the classical Kuiper Belt (semimajor axis range 42-48 au) are both close to (within 0.7 When binning the sample into smaller semimajor axis bins, we find the measured mean plane mostly consistent with both the invariable plane and the theoretically expected Laplace surface forced by the known planets. Statistically significant discrepancies are found only in the semimajor axis ranges 40.3-42 au and 45-50 au; these ranges are in proximity to the ν These results do not support a previously reported anomalous warp at semimajor axes above 50 au. INTRODUCTION Chiang & Choi (2008) posed the question:"If we could map, at fixed time, the instantaneous locations in threedimensional space of all Kuiper Belt objects [KBOs], on what two-dimensional surface would the density of KBOs be greatest?" The authors demonstrated that this surface, also known as the Laplace surface, is given by the Laplace-Lagrange linear secular theory (Murray & Dermott 1999). This theory is based on the time-variable forcing arising from the planets' secular variations; consequently, the local normal on the Laplace surface varies only slowly with time; secular timescales for KBOs are much longer than 10 The Laplace surface for particles within the Kuiper Belt is not a flat plane because it has warps owing to secular resonances in certain localized regions of semimajor axes within the belt where the rate of orbit pole precession coincides with one of the inclination secular mode frequencies of the planets; at large semimajor axes the Laplace surface converges to the solar system's invariable plane.

Despite over three hundred years of effort, no solutions exist for predicting when a general planetary configuration will become unstable. We introduce a deep learning architecture to push forward this problem for compact systems. While current machine learning algorithms in this area rely on scientist-derived instability metrics, our new technique learns its own metrics from scratch, enabled by a novel internal structure inspired from dynamics theory. Our Bayesian neural network model can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both non-resonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to five orders of magnitude faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK package, with training code open-sourced.