Hubble Space Telescope Pointing Performance Due to Micro-Dynamic Disturbances from the NICMOS Cryogenic Cooler
Bibliographic record
Abstract
The Vibration Admittance Test (VET) was performed to measure the emitted disturbances of the Near Infrared Camera and Multi-Object Spectrometer (NICMOS) Cryogenic Cooler (NCC) in preparation for NCC installation onboard the Hubble Space Telescope (HST) during Servicing Mission 3B (SM3B). Details of the VET ground-test are described, including facility characteristics, sensor complement and configuration, NCC suspension, and background noise measurements. Kinematic equations used to compute NCC mass center displacements and accelerations from raw measurements are presented, and dynamic equations of motion for the NCC VET system are developed and verified using modal test data. A MIMO linear frequency-domain analysis method is used to compute NCC-induced loads and HST boresight jitter from VET measurements. These results are verified by a nonlinear time-domain analysis approach using a high-fidelity structural dynamics and pointing control simulation for HST. NCC emitted acceleration levels not exceeding 35 micro-g rms were measured in the VET and analysis methods herein predict 3.1 milli-areseconds rms jitter for HST on-orbit. Because the NCC is predicted to become the predominant disturbance source for HST, VET results indicate that HST will continue to meet the 7 milli-arcsecond pointing stability mission requirement in the post-SM3B era.
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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) | 0.000 | 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) | 0.001 | 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".