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Jackson Craig

Publications and source records attributed to Jackson Craig.

2 recordsLinked to original sources
arXiv2026

Carruthers Data Processing Pipeline: Photon Background Removal

The Carruthers Geocorona Observatory, launched in September 2025, is NASA's first mission devoted to investigating the fundamental nature of Earth's exosphere from its distant vantage in halo orbit around the Earth-Sun Lagrange 1 (L1) point. Its primary payload, the GeoCoronal Imager, consists of two coaligned photometric imagers that measure ultraviolet Lyman-alpha emission radiance from exospheric hydrogen simultaneously at wide- and narrow- fields of view. These observations will map the exosphere's global spatial structure and observe its temporal variability in response to geomagnetic storms. However, a critical step in that analysis is isolating the in-band exospheric hydrogen Lyman-alpha signal from any other source of photons, including in-band InterPlanetary Hydrogen photon background and out-of-band photon backgrounds, which are emitted from Earth's limb. This paper details the algorithms used to retrieve and remove photon backgrounds from the GCI science images acquired on-orbit. Finally, the science data processing pipeline that transforms instrument-effect corrected images (L1B science data product) into absolutely-calibrated exospheric H measurements in physical units (L1C science data product) is detailed. Evaluation of algorithm performance based on a realistic pre-flight case study using synthetic data demonstrates that these photon background removal algorithms achieve high accuracy, leaving a residual systematic bias in isolated exospheric Lyman-alpha of only 3% under beginning-of-life conditions.

astro-ph.IM↗
arXiv2026

Deep RL for Fast Long-Horizon Operations Scheduling on NASA's Carruthers Geocorona Observatory Mission

Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, constraint programming, or manual planning. We present a scalable deep reinforcement learning framework developed and deployed for NASA's Carruthers Geocorona Observatory mission. Our framework introduces a macro-action abstraction known as activity blocks coupled with dynamic action-masking to navigate the intractably large search space and strictly enforce complex power, thermal, and instrument constraints. The resulting architecture generates globally feasible schedules with overwhelming probability, establishes operational trust, and executes a full training cycle in under six hours, circumventing the need for policy robustness by enabling rapid, on-demand retraining. Further, resulting schedules outperform baseline heuristics in scheduled science quality. The deep reinforcement learning framework was deployed as the default operational scheduler for the Carruthers Geocorona Observatory mission from the outset of the mission, demonstrating that deep reinforcement learning can be trusted for real spacecraft operations under complex, evolving constraints.

astro-ph.IM↗