Classification of LEO Satellites Using Occultations of Background Stars

TL;DR

This paper explores a simulation-based method for classifying LEO satellites by analyzing occultations of background stars, highlighting the theoretical feasibility and current observational limitations.

Contribution

It introduces a simulation framework for satellite shape classification using star occultation data and derives the observational requirements for successful implementation.

Findings

Classification is theoretically sound but challenging with current telescopes.

Successful classification needs short exposure times (~0.002s) and high star density (~500 stars).

Method's success rate depends on exposure time and background star density.

Abstract

We present the result of a proof-of-concept simulation designed to classify LEO satellites based on their occultations of background stars. We generate satellite shapes drawn from two broad shape classes, 'boxwing' and 'square'. We then simulate the resulting occultation photometry that would be caused by these satellites orbiting in LEO and intersecting with background stars. The resulting data is then inverted to attempt to recover the input shape and classify the satellite correctly. We find that the technique is theoretically sound, but ambitious with current telescope capabilities. We construct an equation for the required success rate of the method, as a function of exposure time and density of background stars. We find that successful classification requires short exposure times and high background stellar densities. For success rates in excess of 75%, we find a required exposure time of $\sim2.0\times10^{-3}$s, and $\sim500$ stars along the satellites path. Results are presented in terms of these two key parameters, and are discussed in the context of current observational capabilities and alternative satellite characterisation methods.