D. Kucharski

D. Kucharski, A. Gaska, T. Kowaluk et al.

A reproducible deep learning framework is presented for surface metrology to predict surface texture parameters together with their reported standard uncertainties. Using a multi-instrument dataset spanning tactile and optical systems, measurement system type classification is addressed alongside coordinated regression of Ra, Rz, RONt and their uncertainty targets (Ra_uncert, Rz_uncert, RONt_uncert). Uncertainty is modelled via quantile and heteroscedastic heads with post-hoc conformal calibration to yield calibrated intervals. On a held-out set, high fidelity was achieved by single-target regressors (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918), with two uncertainty targets also well modelled (Ra_uncert 0.9899, Rz_uncert 0.9955); RONt_uncert remained difficult (R2 0.4934). The classifier reached 92.85% accuracy and probability calibration was essentially unchanged after temperature scaling (ECE 0.00504 -> 0.00503 on the test split). Negative transfer was observed for naive multi-output trunks, with single-target models performing better. These results provide calibrated predictions suitable to inform instrument selection and acceptance decisions in metrological workflows.

James Allworth, Lloyd Windrim, Jeffrey Wardman et al.

This paper presents the initial stages in the development of a deep learning classifier for generalised Resident Space Object (RSO) characterisation that combines high-fidelity simulated light curves with transfer learning to improve the performance of object characterisation models that are trained on real data. The classification and characterisation of RSOs is a significant goal in Space Situational Awareness (SSA) in order to improve the accuracy of orbital predictions. The specific focus of this paper is the development of a high-fidelity simulation environment for generating realistic light curves. The simulator takes in a textured geometric model of an RSO as well as the objects ephemeris and uses Blender to generate photo-realistic images of the RSO that are then processed to extract the light curve. Simulated light curves have been compared with real light curves extracted from telescope imagery to provide validation for the simulation environment. Future work will involve further validation and the use of the simulator to generate a dataset of realistic light curves for the purpose of training neural networks.