Bright region reset: an on-detector strategy for minimizing the impacts of atmospheric emission lines on spectral observations
Bibliographic record
Abstract
Observations in the near-infrared using large ground-based telescopes are adversely impacted by bright atmospheric emission lines, particularly the OH Meinel bands. These lines can saturate a moderate-resolution spectrograph on the order of minutes, resulting in information loss at the wavelengths of the lines. OH lines also vary on similar timescales, requiring frequent sky exposures to be able to subtract the sky spectrum from that of the target. In this paper we present a new method, which we call bright region reset (BRR), to prevent the saturation of these lines in near-infrared spectra while simultaneously improving information about their variability. This is accomplished by periodically resetting pixels that contain bright lines on a detector capable of sub-window readout while the rest of the detector continues integrating. This method is demonstrated on the McKellar Spectrograph in the 1.2 m telescope at the Dominion Astrophysical Observatory in Victoria, Canada. Using a Teledyne H2RG detector, we reset the emission lines produced by an arc lamp while still recording their flux. We show no degradation in the resulting spectrum compared to a conventional observing mode. Unlike other OH line mitigation strategies, the BRR method not only avoids loss of information at wavelengths containing the lines, but also provides higher-cadence information on sky line variability, making it a promising technique for implementation at observatories. We advocate demonstrating this method on sky at existing 8--10 m class facilities with near-infrared spectrographs equipped with HxRG detectors in order to test its feasibility for use in sky subtraction schemes for premier modern spectrographs, including the upcoming generation of instruments for the Extremely Large Telescopes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".