Asteroid Albedo and Size Inversion: A Deep Learning Method and Application Based on Multiparameters

Asteroids, algorithms, and explainability: a unified framework for efficient classification of near-Earth objects

Generating Two-Line Elements of Celestial Objects From Astronomical Images Using Deep Neural Networks

Accurate satellite tracking requires up-to-date Two-Line Elements (TLEs), as outdated data can lead to significant positioning errors. While ground-based optical telescopes are highly accessible, generating TLEs from their data is complicated by the fundamental lack of direct range measurements. This paper presents an automated end-to-end pipeline designed to overcome this limitation by proposing a robust method to estimate the range from prior TLE. The pipeline can then generate the updated TLEs by calculating new satellite state vectors using the estimated range. The pipeline consists of star and satellite detection, astrometric calibration, and orbit determination. For star and satellite detection, the key component of the pipeline is a robust deep learning-based detection model. To achieve this, we benchmarked models such as Deformable DETR, RF-DETR, and YOLOv12 against traditional image processing methods with 3105 images in FITS (Flexible Image Transport System) format. RF-DETR yields the highest F1 score (0.93) and precision (0.97) at an 8-pixel threshold. The detected star coordinates resulting from using RF-DETR, the best-performing model, were fed into Astrometry.net for precise astrometric calibration to determine the satellite celestial coordinates. The range required for orbit determination was estimated by extracting the prior TLE and propagating it to the observation epoch via SGP4. The satellite state vectors were then calculated using TLE-constrained orbit determination approach using the estimated range, followed by an inverse SGP4 optimization to recover the mean orbital elements. The generated TLEs were validated against public TLEs from Space-Track.org. To evaluate this pipeline, updated TLEs were generated specifically for medium Earth orbit (MEO) and geostationary Earth orbit (GEO) targets. The results demonstrate that the proposed method yields high accuracy for GEO satellites, achieving a mean motion difference of 0.0030 rev/day and a 24-hour ground-track position error of 0.83 degrees. In comparison, MEO satellites achieve a mean motion difference of 0.0126 rev/day and an error of 5.27 degrees. These results suggest that the proposed pipeline provides a robust foundation for automated orbit determination with clear potential for further refinement.

A Machine Learning Based Search for Lunar Anomalies

The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, amassing a large dataset of images and offering researchers the opportunity to study the surface of the Moon at unprecedented scale. Here, we aim to test the abilities of the Beta-Variational Autoencoder (VAE) created by Lesnikowski et al. (2024), an unsupervised learning model which identifies anomalous features across the Moon's surface, locating not only scientifically useful geologic formations such as rockfall deposits, fresh impact craters, irregular mare patches, or volcanic pits/collapsed lava tubes, but also artificial objects such as landed spacecraft. This investigation further gauged the model's ability to locate anomalous surface features, successfully recovering two places of interest (Plaskett Crater and Paracelsus C Crater) and numerous landed technological assets at a statistically significant rate.

ADORA: a differentiable optical modeling and astrometric retrieval framework for SHERA

ADORA provides a flexible framework for studying astrometric extraction, calibration-bias diagnosis, and future SHERA requirements, and Pixel-position errors across the tested range remain near the matched-model recovery scale indicating robustness to certain detector calibration errors.

Improved ephemerides for major targets of the Emirates Mission to the asteroid belt from multi-source astrometry

Precise orbit determination of main-belt asteroids is essential for deep-space mission design, navigation, and target characterization. This study presents a refined orbit-determination analysis for 623 Chimaera and 269 Justitia, the two largest targets of the Emirates Mission to the Asteroid Belt (EMA). The dynamical model includes perturbations from sun, major planets, Pluto, selected massive asteroids, selected Kuiper Belt Objects, and solar radiation pressure. To improve computational efficiency while retaining the dominant dynamical effects, we developed a perturber-selection scheme based on a perturbing-acceleration indicator and a knee-point criterion. Long-arc ground-based optical astrometry was combined with high-precision measurements from Gaia, TESS, WISE, and stellar occultations. Catalogue-dependent debiasing, residual-based outlier rejection, and station-dependent weighting were applied to mitigate the heterogeneity of the astrometric data. The resulting solutions were evaluated using post-fit residual statistics, covariance propagation, and comparisons with reference ephemerides generated through JPL Horizons. The differences between the derived orbits and the reference ephemerides remain at the kilometre-to-tens-of-kilometres level over the investigated interval, corresponding to small angular offsets at the relevant geocentric distances. A sensitivity and covariance analysis was also conducted to assess the observability of solar radiation pressure. The results indicate that milliarcsecond-level astrometry is required for a reliable estimation of the radiation-pressure coefficient; therefore, this coefficient was fixed to its nominal theoretical value in the adopted solutions. These results provide a dynamical and observational basis for further ephemeris refinement of EMA target asteroids.

Structural and Photometric Parameter Estimation of Low-surface-brightness Galaxies Using a Deep Learning Framework

Low-surface-brightness galaxies (LSBGs) play an important role in studies of galaxy formation and evolution, yet accurate measurements of their structural and photometric parameters remain challenging for conventional analysis pipelines due to their diffuse light distributions and low signal-to-noise ratios. In this work, we present an automated deep learning framework, LSBGPENet, for the robust estimation of structural and photometric parameters of LSBGs from wide-field imaging data. The framework directly operates on galaxy image cutouts and simultaneously infers key parameters, including total magnitude (m), effective radius (Reff), ellipticity (ϵ), and Sérsic index (n), together with associated uncertainty estimates. Mean and central surface brightnesses (μeff, μ0) are subsequently derived from the inferred parameters. We assess the accuracy and reliability of the inferred parameters through comparisons with traditional profile-fitting measurements. On both simulated data and observational data from the Dark Energy Survey, the framework achieves high predictive accuracy, with mean coefficients of determination of 0.91 and 0.94, respectively, and well-calibrated uncertainty estimates, characterized by mean uncertainty calibration errors of 0.004 and 0.009. The inferred parameters are statistically consistent with those obtained from GALFIT. The proposed framework provides a scalable and reproducible solution for structural and photometric parameter estimation of LSBGs and is well suited for application to current and forthcoming wide-field surveys, including the China Space Station Telescope.