Modeling and Control for Fine Pointing and Jitter Management in Balloon-Borne Telescopes
Bibliographic record
Abstract
The Super-pressure Balloon-borne Imaging Telescope (SuperBIT) and its upcoming successor, the Gigapixel Balloon-borne Imaging Telescope (GigaBIT), are advanced astronomical instruments that operate above 99% of the Earth's atmosphere, benefiting from significantly improved imaging conditions. To meet their stringent pointing requirements, SuperBIT and GigaBIT are equipped with a Fine Guidance System (FGS) that employs precision opto-mechanical components, such as a Fast Steering Mirror (FSM), to correct for jitter and achieve sub-arcsecond image stability. This thesis focuses on the comprehensive modeling, control, and systems engineering of the Fine Guidance System of GigaBIT, incorporating insights and lessons learned from SuperBIT. A high-fidelity integrated model of the SuperBIT FGS is developed using first principles and line-of-sight ray tracing theory, and scaled to the specifications of GigaBIT. FGS data from SuperBIT's 2023 science flight is analyzed and incorporated into the simulation framework. Various control strategies, including Proportional (P), Proportional-Integral-Derivative (PID), and Linear Quadratic Regulator (LQR) controls, are explored and evaluated for their effectiveness in managing jitter. Both PID and LQR are demonstrated to be effective options for GigaBIT. Finally, this thesis conducts a Phase A study for GigaBIT, employing a model-based systems engineering approach to define a system architecture and outline top-level and subsystem-level requirements. GigaBIT, building on the SuperBIT legacy, is set to enhance high-resolution astronomical imaging, marking a significant advancement in the field of balloon-borne telescopes. Le Super-pressure Balloon-borne Imaging Telescope (SuperBIT) et son successeur à venir, le Gigapixel Balloon-borne Imaging Telescope (GigaBIT), sont des instruments astronomiques avancés qui opèrent au-delà de 99\% de l'atmosphère terrestre, bénéficiant de conditions d'imagerie améliorées. Pour répondre à leurs exigences strictes en matière de pointage, SuperBIT et GigaBIT sont équipés d'un Système à Guidage Fin (SGF) qui utilise des composants opto-mécaniques de précision, tels qu'un miroir de direction rapide, pour corriger les vibrations et obtenir une stabilité d'image d’une fraction d’arcseconde. Cette thèse se concentre sur la modélisation, l'asservissement et l'ingénierie des systèmes du SGF de GigaBIT, en incorporant les connaissances et les leçons tirées de SuperBIT. Un modèle intégré haute-fidélité du SGF de SuperBIT est développé en utilisant des principes fondamentaux et la théorie du tracé des rayons, puis ajusté aux spécifications de GigaBIT. Les données SGF du vol scientifique de 2023 de SuperBIT sont analysées et incorporées dans le cadre de la simulation. Diverses stratégies d’asservissement, y compris les contrôles Proportional (P), Proportional-Integral-Derivative (PID) et Linear Quadratic Regulator (LQR), sont explorées et évaluées pour leur efficacité à gérer les vibrations. Les contrôles PID et LQR se sont avérés être des options efficaces pour GigaBIT. Enfin, cette thèse réalise une étude de Phase A pour GigaBIT, en utilisant une approche d'ingénierie des systèmes pour définir une architecture et établir des exigences au niveau du système et des sous-systèmes. GigaBIT, tout comme SuperBIT, vise à améliorer l'imagerie astronomique haute résolution, marquant ainsi une avancée majeure dans le domaine des télescopes embarqués sur ballons stratosphériques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".