LISA dynamics and control: Closed-loop simulation and numerical demonstration of time delay interferometry
Bibliographic record
Abstract
The Laser Interferometer Space Antenna (LISA), space-based gravitational wave observatory involves a complex multidimensional closed-loop dynamical system. Its instrument performance is expected to be less efficiently isolated from platform motion than was its simpler technological demonstrator, LISA Pathfinder. It is of crucial importance to understand and model LISA dynamical behavior accurately to understand the propagation of dynamical excitations through the response of the instrument down to the interferometer data streams. More generally, simulation of the system allows for the preparation of the processing and interpretation of in-flight metrology data. In this work, we present a comprehensive mathematical modeling of the closed-loop system dynamics and its numerical implementation within the LISA Consortium simulation suite. We provide, for the first time, a full time-domain numerical demonstration of postprocessing time delay interferometer techniques combining multiple position measurements with realistic control loops to create a synthetic Michelson interferometer. We show that in the absence of physical coupling to spacecraft and telescope motion (through tilt-to-length, stiffness, and actuation cross-talk) the effect of noisy spacecraft motion is efficiently suppressed to a level below the noise originating in the rest of the instrument.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".