Thermal infrared characterization of spatially unresolved resident space objects: Prospects from analytical two-component modeling

TL;DR

This paper explores a two-component thermal IR spectral model to remotely determine the temperature characteristics of unresolved space objects, enhancing satellite identification and status assessment from space telescope data.

Contribution

It introduces an analytical two-greybody spectral model to better distinguish between natural and artificial space objects using unresolved thermal IR observations.

Findings

The model can identify multiple temperature components in satellite spectra.

Discrimination between natural rocks and satellites improves with higher signal-to-noise ratios.

Potential applications include characterizing objects with unresolved photometry from space telescopes.

Abstract

In this work we investigate the potential of a thermal infrared (IR) space telescope to remotely characterize the component temperatures of a satellite. With the rapid increase in the number of objects launched in recent years, the ability to detect, track, identify and determine the intent of satellites has become of increasing importance. Spectral modeling of satellites from multi-wavelength photometry in the thermal IR is a technique that has the potential to derive information about the temperature and operational status of a satellite in orbit, without the requirement to spatially resolve the target. Previous work has focused on determination of a single/effective temperature for a Resident Space Objects (RSOs) - such as satellites, asteroids, debris and rocket bodies - from remote observations, obtaining mixed results in terms of ability to classify objects. To progress, we explore a two-greybody component spectral model. Using this analytical model, we investigate which temperature characteristics may be identified from unresolved multi-wavelength photometric observations as a function of the signal-to-noise ratio, under the assumption of Poisson noise-dominated data. With this instrument-agnostic framework, we then quantify the potential of this model to discriminate between RSOs with a single temperature (e.g. natural rocks) versus human-made satellites with a chassis and deployed solar panels where significant component temperature differences exist under typical orbital configurations. Last, we comment on promising prospects of this model for applications to existing and future space telescope observations to characterize RSOs from spatially unresolved photometry