arXiv subjects

Christian Wöhler

Publications and source records attributed to Christian Wöhler.

4 recordsLinked to original sources

arXiv2026

Verifiably grounded machine interpretation of lunar geology

Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations. Here, we investigate how far this interpretive workflow can be automated by a multimodal vision-language model. Focusing on the stratigraphy of lunar basaltic mare volcanism, we train a model to generate verifiably grounded geologic interpretations directly from co-registered topographic, spectral, and geologic maps. We demonstrate that while the system successfully balances established geological priors with local visual evidence to accurately describe stratigraphy and terrain, numeric age dating derived solely from vision defaults to memorized priors. Integrating an open-book retrieval mechanism resolves this, enabling the model to faithfully cite published chronologies. Our findings delineate the necessary architecture for automated geologic inference: site evidence must be visually interpreted from local data, while quantitative historical context must be retrieved from the scientific record.

cs.CL↗

arXiv2026

Wide-field Polarization Imaging and Numerical Modeling of the Coma and Tail of Comet C/2023 A3 (Tsuchinshan-ATLAS)

Imaging polarimetry enables the spatially resolved investigation of cometary dust properties across different morphological structures. While cometary comae have been studied thoroughly in the pertinent literature, cometary tails have remained less explored. Comparing these regions can reveal differences in the size, structure, and composition of their dust. The goal of this study is to examine the size, structure and composition of the dust particles in the coma and in particular in the tail of the bright comet C/2023 A3 (Tsuchinshan-ATLAS) and to infer possible differences. For this purpose, we rely on the method of telescopic wide-field polarimetric imaging of the comet in the visible to near-infrared domain in order to obtain the dependence of the degree of linear polarization (DoLP) of the coma and tail on the phase angle across a broad range. An off-the-shelf industrial grade polarization camera was used in combination with a telescope of short aperture ratio. These observations are complemented by T-matrix and Discrete Dipole Approximation modeling using the MSTM5 and DDSCAT software framework, respectively, for simulation of light scattering by dust particles of fractal agglomerate and agglomerate debris morphology. Our observations indicate that the coma exhibits a high maximum DoLP of 0.34, which is further exceeded by a factor of about two by the DoLP of the comet's tail. Our modeling results suggest a 50:50 olivine-carbon composition. The fraction of agglomerate debris was found to be 50% in the coma and possibly higher in the tail. The differences between coma and tail in the observed maximum DoLP and the phase angle at which it occurs can be explained by a predominance of particles with radii larger than 0.6 micrometer in the coma vs. smaller sub-micrometer particles close to the Rayleigh limit in the tail [...]

astro-ph.EP↗

arXiv2025

The Moon's Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction

Multimodal learning is an emerging research topic across multiple disciplines but has rarely been applied to planetary science. In this contribution, we propose a single, unified transformer architecture trained to learn shared representations between multiple sources like grayscale images, Digital Elevation Models (DEMs), surface normals, and albedo maps. The architecture supports flexible translation from any input modality to any target modality. Our results demonstrate that our foundation model learns physically plausible relations across these four modalities. We further identify that image-based 3D reconstruction and albedo estimation (Shape and Albedo from Shading) of lunar images can be formulated as a multimodal learning problem. Our results demonstrate the potential of multimodal learning to solve Shape and Albedo from Shading and provide a new approach for large-scale planetary 3D reconstruction. Adding more input modalities in the future will further improve the results and enable tasks such as photometric normalization and co-registration.

cs.CV↗

arXiv2024

RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications

A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented. Data provided by four series radar sensors mounted on one test vehicle were recorded and the individual detections of dynamic objects were manually grouped to clusters and labeled afterwards. The purpose of this data set is to enable the development of novel (machine learning-based) radar perception algorithms with the focus on moving road users. Images of the recorded sequences were captured using a documentary camera. For the evaluation of future object detection and classification algorithms, proposals for score calculation are made so that researchers can evaluate their algorithms on a common basis. Additional information as well as download instructions can be found on the website of the data set: www.radar-scenes.com.

cs.LG↗