Performance loss and recovery of virtually imaged phased arrays with imperfect mirror parallelism
Bibliographic record
Abstract
Practical defects in parallel-plate interferometers reduce instrument finesse, compromising spectral resolution, and can distort measured spectral lineshapes. The effect of imperfect mirror parallelism is usually described by a single broadened instrument response function and diminished finesse value. However, such a characterization fails to capture spatial field effects from dispersive interferometers, such as the virtually imaged phased array (VIPA). To explore these effects, a model is developed to compute fringe patterns formed by VIPAs with nonparallel mirrors and imaged along planes at specifiable distances from the paired imaging lens. Following validation against an established VIPA model and standard etalon theory with ideal parallel-plate configurations, the new model is used to examine the effects of mirror nonparallelism. While it captures the general loss of instrument performance in spectral resolution and finesse, it also reveals fringe lineshape distortions and nonuniformity of resolution and intensity scaling across the measurement field. Furthermore, when the measurement plane is decoupled from the back focal plane of the lens, there is an evolution of field behavior, and near-ideal instrument performance is recovered within a limited region of the measurement field. Results compare favorably to Zemax simulations and experimental data. This work identifies and characterizes nonideal optical behavior arising from practical defects in mirror parallelism, thereby enabling recognition of measurement artifacts, and imparts remedial measures for selective recovery of instrument finesse.
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.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".