Here we present a concept for a mobile, completely off-grid, robotic observatory for rapid deployment and observational support. This 1-meter aperture, 3-degree FOV telescope employs state-of-the-art commercial instrumentation such that it not only supports satellite orbit cataloging but also closely spaced object detection/ characterization at atmospheric seeing limits, i.e. sub-arcsecond pixels vs. more traditional cataloging systems’ 2-3 arcsecond pixels. Its relatively large étendue, high throughput, and up to 50 deg/s slew speeds provides for high survey speeds, be it for lost space debris or astronomical transients. We will detail the design and simulated performance of this Deployable, Attritable Optical (DAO) system. Furthermore, each system will employ US Space Force developed observatory control software called SensorKit, a completely open-source software that enables robotic operation and, if desired for SDA purposes, communication with the Unified Data Library. Scheduling, tasking, data processing and dissemination and more are a part of the US Space Force MACHINA program, presented separately in these proceedings.
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.
Recent work demonstrates that convolutional neural networks can be trained to recognize artificial satellites from spatially unresolved ground-based observations (SpectraNet). SpectraNet enables space domain awareness (SDA) catalogs to be enriched with object identity, a critical source of information for space domain stakeholders. As learned spectral SDA matures, conditions for training and deploying performant and calibrated neural network recognition algorithms must be measured. In this work we present a simulated three year baseline of observations using a longslit spectrograph on a single telescope. We use this dataset to develop a framework for measuring baseline data requirements for performant SpectraNet models, and for testing the performance of those models after deployment. On this limited (single telescope, longslit spectrograph) setup, the presented framework returns a performant model after three weeks of collections. Further, we find that a model can be deployed for a full annual cycle after twenty six weeks of data collection, and the model reaches maximum sustained inference performance after a year. Thus a SpectraNet powered longslit spectrograph can provide tactical inferences after a few weeks and be retrained to infer through seasonal variability during deployment. We find that the simulated system and dataset regularly exceed 82% classification accuracy, and discuss performance improvements with enhanced instrumentation and/or multi-telescope networks.
We introduce two new tools to the application of polarimetry to space domain awareness (SDA), the LoVIS spectropolarimeter on the 3.6 m AEOS telescope and deep convolutional neural networks (CNNs). Using a dataset of 20,000 simulated satellite observations, we train a CNN to map distance-invariant spectropolarimetric data to object identity. We report the classification accuracy of this simulation for a 9-class satellite problem, comparing results against low-resolution spectra for which prior success has been demonstrated as well as solar phase angle and satellite apparent magnitude. These initial experiments show potential for improved discrimination against nearly identical satellites on the basis of added polarimetric data.
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.
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