The Dragonfly Spectral Line Mapper: completion of the 120-lens array
Bibliographic record
Abstract
The Dragonfly Spectral Line Mapper is a mosaic telescope comprising 120 Canon telephoto lenses, based on the design of the Dragonfly Telephoto Array. With a wide field of view, and the addition of the “Dragonfly Filter-Tilter” instrumentation holding ultra narrow bandpass filters in front of each lens, the Dragonfly Spectral Line mapper is optimized for ultra low surface brightness imaging of visible wavelength line emission. The Dragonfly Spectral Line Mapper was constructed and commissioned in four phases from March 2022 to November 2023. During this time, four individual mounts of 30 lenses each were constructed and commissioned. The commissioning of the telescope included the deployment of the “Dragonfly StarChaser” which carries out image stabilization corrections in the telephoto lens, to enable hour-long exposures to be taken. In addition, we introduced new instrumentation such as a film to cover the optics to keep the filters clean. Here we describe the updated design of the complete 120-lens array, and the implementation of the instrumentation described above. Additionally, we present updated characterization of the cameras and filter transmission for the full array. Finally, we reflect on the construction and commissioning process of the complete 120-lens array Dragonfly Spectral Line Mapper, and remark on the feasibility of a larger 1000-lens array.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".