Limiting atmospheric emission lines with on-detector subarrays
Bibliographic record
Abstract
Observations in the near-infrared using large ground-based telescopes are limited by bright atmospheric emission lines, particularly OH lines, which can saturate a spectrograph on the order of minutes. Longer exposures will not contain useful information about the emission lines and also run the risk of detector effects such as bleeding and persistence. By using guide windows on a HAWAII-2RG infrared detector, we demonstrate on-detector suppression of these bright lines in long exposures. This is achieved by periodically resetting detector regions which contain bright emission lines before they have the chance to saturate, while the rest of the detector continues integrating. Used with extended exposure lengths, this could allow for significant reduction of the read noise overhead required for stacking shorter exposures. In addition, through non-destructive reading we are able to monitor the lines which are being reset, allowing us to retain information about the characteristics and variability of these lines. We present the results of a first demonstration of this technique using controlled observations of arc lamps with the 1.2-m McKellar Spectrograph at the Dominion Astrophysical Observatory in Victoria, Canada. We find promising results for the potential future use of this technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".