Speaker
Description
The growing density of space debris in Earth orbit increases the need for reliable techniques to characterize Resident Space Objects (RSOs) beyond purely orbital information. This work presents a modular simulation framework for generating synthetic optical light curves, based directly on ESA’s DRAMA suit. Through ongoing collaboration and contributions by OKAPI:Orbits to DRAMA’s development, the framework integrates seamlessly with its components, including orbit propagation, observation modeling, and reflectance estimation.
The resulting framework enables realistic, configurable light‑curve generation for custom objects of varying material properties, shapes, and attitudes. This can be used to form a controlled dataset for evaluating debris‑characterization methods and ties directly to the current updates to ESA’s debris population MASTER led by OKAPI:Orbits, where the debris objects are extended by material and shape.
To demonstrate the frameworks capabilities, two exemplary parameter‑recovery strategies are explored utilizing the framework. The first is a classification approach using Long Short-Term Memory (LSTM) neural networks, and the second is an inversion strategy employing Gaussian Process Optimization to fit synthetic curves to specific observations. Furthermore, a validation against real light curves from the nine-channel optical wide-angle monitoring system Mini‑Mega-TORTORA (MMT-9) demonstrates both the potential and current limitations of DRAMA‑based simulation framework for inferring physical properties of RSOs.
This synthetic light curve generation framework provides a foundation for future advancement of space‑debris characterization techniques and supports ongoing efforts to enhance debris‑environment models and operational risk‑assessment tools.