Precise pointing and stabilization performance for the balloon-borne imaging testbed: 2015 test flight
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Balloon-borne astronomy offers an attractive option for experiments that require precise pointing and attitude stabilization, due to a large reduction in the atmospheric interference observed by ground-based systems as well as the low-cost and short development time-scale compared to space-borne systems. The Balloon-borne Imaging Testbed (BIT) is an instrument designed to meet the technological requirements of high-precision astronomical missions, and is a precursor to the development of a facility-class instrument with capabilities similar to the Hubble Space Telescope. The attitude determination and control systems (ADCS) for BIT, the design, implementation, and analysis of which are the focus of this paper, compensate for compound pendulation effects and other sub-orbital disturbances in the stratosphere to within 1–2′′ (rms), while back-end optics provide further image stabilization down to 0.05′′ (not discussed here). During the inaugural test flight from Timmins, Canada in September 2015, BIT ADCS pointing and stabilization performed exceptionally, with coarse pointing and target acquisition to within <0.1° and fine stabilization to 0.68′′ (rms) over long (10–30 min) integrations. This level of performance was maintained during flight for several tracking runs that demonstrated pointing stability on the sky for more than an hour at a time. To refurbish and improve the system for the three-month flight from New Zealand in 2018, certain modifications to the ADCS need to be made to smooth pointing mode transitions and to correct for internal biases observed during the test flight. Furthermore, the level of autonomy must be increased for future missions to improve system reliability and robustness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it