Speaker
Description
As space activity grows exponentially, the orbital environment is becoming increasingly congested with active satellites, inactive spacecraft, and debris of varied sizes and shapes. Ensuring the sustainability of future space missions amid this swarm of objects demands precise knowledge of each object’s position, shape, and rotational state.
Despite the significant efforts by space surveillance networks to monitor and track a growing number of objects, the available data often consists only of positional information, leaving critical gaps in understanding the physical and rotational characteristics of defunct satellites and space debris. These parameters, however, are essential for effective space debris removal and accurate assessment of collision risks.
To bridge this knowledge gap, we are leveraging data from the VST/OmegaCam archive, a unique dataset of over 400'000 high-precision observations spanning 12 years. While VST was designed for deep-sky surveys, space debris cross its field of view, leaving detectable traces in the images. The instrument's exceptional sensitivity allows us to detect objects as small as 5 cm in low-Earth orbit (LEO) and 30 cm in geostationary orbit (GEO). Processing this extensive archive requires advanced image analysis, for which we have developed a novel streak-detection method that combines a convolutional neural network with a Hough transform layer. This talk will present our training dataset, algorithm design, and streak-detection workflow.
Following streak detection, we apply a photometric reduction pipeline to extract light curves, revealing insights into each object's attitude and shape. Where possible, these detections are also correlated with existing catalogs. A second presentation will delve into this photometric reduction and catalog correlation process.
