arXiv subjects

Lara Waldrop

Publications and source records attributed to Lara Waldrop.

7 recordsLinked to original sources

arXiv2026

TomoSphero: Fast Differentiable Projector for Planetary and Solar Tomography on Spherical Grids

Computational tomography is a tool for determining the internal structure of objects from a set of projections, typically taken along some regular path. In recent years, methods and GPU-accelerated libraries have emerged that allow for fast reconstruction from projections along more complicated paths. Most of these libraries rely on a Cartesian discretization of the object, which is not appropriate for all scenarios. We present TomoSphero, a differentiable tomographic projector over spherical grids which are often used in planetary and solar tomography. TomoSphero is designed to be used as a building block in reconstruction algorithms and includes common projection types such as cone-beam and parallel-beam, but is flexible enough to accommodate arbitrary projections. TomoSphero is implemented in PyTorch which allows for fast projection computation on GPUs, easy access to modern machine learning optimizers, and automatic differentiation for rapid prototyping of parametric models.

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arXiv2026

Numerical Model Simulation of the Carruthers GCI Images

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arXiv2026

Design and Performance of the Carruthers Geocoronal Imager

The GeoCoronal Imager (GCI) onboard the Carruthers Geocorona Observatory is the primary scientific instrument of the mission. It is designed to measure far ultraviolet light at 121.6 nm (Lyman-alpha) emitted by hydrogen (H) atoms in Earth's exosphere with the sensitivity, accuracy and precision to meet the mission's scientific objectives regarding the nature of terrestrial exospheric structure and dynamics on both global and regional scales. The GCI is comprised of two co-aligned UV imaging systems. The Narrow Field Imager (NFI) acquires nearly continuous images of exospheric Lyman-alpha radiance near and above the Earth's limb at relatively high spatial and temporal resolution, while the Wide Field Imager (WFI) uses relatively higher optical sensitivity and a wider field of view to detect faint Lyman-alpha emission from the exosphere's outermost extent. Both imaging channels feature identical active pixel sensor cameras, gain-intensifiers, and 6-position optical filter wheels. This paper outlines the instrument design requirements, informed by mission science goals, as well as its performance as measured in the vacuum ultraviolet laboratory test and calibration.

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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.

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arXiv2026

Carruthers Data Processing Pipeline: Radiometric Responsivity Calibration

The Carruthers Geocorona Observatory is NASA's first mission dedicated to investigating the fundamental nature of Earth's exosphere and its dynamic response to space weather. Its primary payload, the GeoCoronal Imager, consists of two co-aligned broadband photometric imagers that support simultaneous, common-volume sensing of ultraviolet emission at 121.6 nm (Lyman-alpha) by exospheric hydrogen atoms. Accurate exospheric parameter retrieval from these images requires accurate knowledge of the instrument's optical responsivity, which enables conversion of measured exospheric signal rates into the scientifically relevant quantity of emission radiance. The Carruthers mission achieves absolute sensitivity calibration through the acquisition of photometric measurements of stars and subsequent inversion of the observed fluxes to retrieve the wavelength-dependent responsivity across the passband for each imaging configuration. The retrieval algorithm performance is enhanced by its incorporation of an objective, algorithm-driven ranking criterion to systematically select target calibration stars from a stellar spectral library. The end-to-end workflow, from the stellar selection criterion to the passband inversion, is validated using synthetically generated stellar measurements. These validation tests establish that our optical responsivity retrieval approach has high recovery fidelity, achieving error rates of <5% at the Lyman-alpha wavelength for all primary science imaging modes.

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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.

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arXiv2026

On-orbit Calibration of the Carruthers GCI: Instrument Effect Correction

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 L1 Lagrange 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. The imagers use Micro Channel Plate intensified Complimentary Metal Oxide Semiconductor detectors, which are known to add various artifacts to the final image telemetered from the spacecraft, hereby known as instrument effects. This paper details the algorithms used to retrieve and remove instrument effects from raw telemetry on-orbit, including detector voltage bias, thermal dark current, particle radiation, flat-field, and distortion. Finally, the science data processing pipeline from raw telemetry to instrument-effect corrected images is detailed. Algorithm performance is measured via a synthetic numerical image generator or validated on pre-launch experiments.

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