Recent work demonstrates recognition of artificial satellites in spatially unresolved observations by utilizing learned spectroscopic classification (SpectraNet1 ). That proof of concept exposes critical identifying information currently lacking in catalogs used by space domain awareness stakeholders. In this work we present experiments to increase the accessibility and efficiency of SpectraNet enabled systems by probing the bandpass and resolution requirements for learned recognition of satellites. To enable affordable, off the shelf instrumentation, this work focuses on wavelength ranges accessible by Silicon-based detectors (400-1000 nanometers). While the SpectraNet proof of concept utilized a medium resolution spectrograph on a 3.6 meter telescope at 10,000 feet elevation, we show that the identifying spectral features relate to an object’s overall spectral energy density and are accessible at significantly lower spectral resolution. This finding relaxes the need for large telescopes at high altitude. We further demonstrate that the technology can be utilized via simultaneous multi-band filter photometry. Design considerations for properly obtaining simultaneous photometry are discussed. Thus this work demonstrates that−in simulation−learned spectral recognition is an effective technology from high resolution spectrographs through simultaneous multi-filter photometric instruments. We provide experiments to understand the minimum engineered system needed to perform effective learned recognition, such that the technology can be hardened and widely proliferated.
The detection of closely spaced artificial satellites informs tactical decision making in a high risk scenario in the space domain. In regimes where spatial information is lost (ground observations of small or distant satellites), spectroastrometry simulations have demonstrated the potential to detect the presence of multiple objects down to 0′′.05–ten meters at geostationary orbit–using a medium resolution optical spectrograph on a large aperture telescope.1 This technique falls into the growing field of learned space domain awareness: leveraging convolutional neural networks to rapidly infer tactical information from complex, non-intuitive data. In this work we present a field rotation nodding technique that removes the need for a priori knowledge of the closely spaced object on sky orientation. We discuss modifications to an optical spectrograph necessary to perform this technique. We present simulated bounds on the effectiveness of spectroastrometry for the detection of closely spaced objects.
Effective space domain awareness (SDA) requires accurate positions and identities of artificial satellites. These measurements–critical to effective decision making in the high risk on orbit environment–are daunting in the deep space geosynchronous (GEO) regime. Here, distance precludes collection of spatially resolved measurements from ground-based telescopes. Neural networks designed for deep space object detection and spectroscopic positive identification have been shown to be effective tools for these mission critical SDA measurements. In this work we demonstrate the potential of slitless field spectroscopy to provide simultaneous object detection and identification of on orbit assets at GEO. Slitless spectrographs expose the reflection physics needed for spectroscopic positive identification without destroying the spatial information used for object detection. Such systems are compact and hardened in comparison to classic spectrographs, and may be deployed to small telescopes. In this work we present a GPU-accelerated simulation environment for the production of realistic synthetic imagery to support generation of large datasets for deep learning. We establish a baseline for simultaneous detection and identification performance by training convolutional neural networks on synthetic datasets created with this tool. This work reduces risk for initial technology development and dataset collection, and provides constraints to the design and development of slitless spectrograph systems for space domain awareness.
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