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VTXO: the Virtual Telescope for X-ray Observations

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with 1 km focal length and with nearly 50 milli-arcsecond angular resolution. Laboratory characterization of PFLs have demonstrated near diffraction-limited angular resolution in the X-ray band, but they require long focal lengths to achieve this quality of imaging. VTXO is formed by using precision formation flying of two SmallSats: a smaller, 6U OpticsSat that houses the PFLs and navigation beacons while a larger, ESPA-class DetectorSat contains an X-ray camera, a charged-particle radiation monitor, a precision star tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly-elliptical supersynchronous geostationary transfer orbit to allow the inertial formation to form and hold around the 90,000 km apogee for 10 hours of the 32.5-hour orbit with nearly a year mission lifetime. The guidance, navigation, and control (GN&C) for the formation flying uses standard CubeSat avionics packages, a precision star tracker, imaging beacons on the Optics Sat, and a radio ranging system that also serves as an inter-satellite communication link. VTXO’s fine angular resolution enables measuring the environments nearly an order of magnitude closer to the central engines of bright compact X-ray sources compared to the current state of the art. This X-ray imaging capability allows for the study of the effects of dust scattering nearer to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure nearer to the compact object in X-ray novae such as Cyg X-1and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The In this paper, the VTXO science performance, SmallSat and instrument designs,and mission description is be described. The VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions.

John Krizmanic↗

VTXO: The Virtual Telescope for X-ray Observations

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with⇠1 km focal length and with⇠50 milli-arc second angular resolution. VTXO is formed by using precision formation flying of two SmallSats: a smaller OpticsSat that houses the PFLs and navigation beacons while a larger DetectorSat contains an X-ray camera, a precision start tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly elliptical super synchronous orbit allow the formation to hold in an inertial frame around the 90,000 km apogee for 10 hours of the 32.5 hour orbit with nearly a year mission lifetime. VTXO’s fine angular resolution enables measuring the environments close to the central engines of bright compact X-ray sources. This X-ray imaging capability allows for the study of the effects of dust scattering near to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure near to the compact object in X-ray novae such as Cyg X-1 and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The VTXO SmallSat and instrument designs, mission parameters, and science performance are described. VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions

John F Krizmanic↗

VTXO: Virtual Telescope for X-ray Observations

VTXO is a potential SmallSat precision formation flying mission using an OpticsSat with X-ray Phase Fresnel Lens (PFL) Optics and a DetectorSat with an X-ray camera forming a 1km focal length X-ray telescope with 55 milli-arcsecond(mas) angular resolution and 8 arcsecond FoV. VTXO development was supported under a 2018 NASA Astrophysics Science SmallSat Studies proposal.

John Krizmanic↗

Trajectory Optimization for the Virtual Telescope for X-Ray Observations

The Virtual Telescope for X-Ray Observations (VTXO) is a long focal length telescope which promises to provide orders of magnitude improvement in angular resolution in the X-ray band. VTXO will include a Phased Fresnel Lens (PFL), which provides nearly diffraction-limited imaging, with a 1 km focal length. The PFL is carried by the Optics Spacecraft, which flies in a formation with the Detector Spacecraft, approximating a rigid telescope body. In order to maintain the formation requirements, while pointing the telescope axis at the desired astronomical targets, one spacecraft will be traveling on a non-natural trajectory, requiring the vehicle to maneuver regularly to maintain the telescope pointing. If care is not taken in the trajectory design, these paths result in large propellant consumption. However, there is an opportunity to optimize trajectories when re-arranging the formation between different astronomical targets. This paper presents an optimization scheme for re-pointing the telescope, utilizing a non-traditional path-based cost function to solve the propellant optimal trajectory. The resulting trajectories show a factor of four improvement in propellant consumption compared to the baseline. The optimization techniques developed for VTXO are applicable to orbits ranging from low-Earth orbit, to highly eccentric Earth orbits, and Lagrange point orbits.

Kyle Rankin↗

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T , consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

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