ANDROMEDA (M31) OPTICAL AND INFRARED DISK SURVEY. I. INSIGHTS IN WIDE-FIELD NEAR-IR SURFACE PHOTOMETRY
Bibliographic record
Abstract
We present wide-field near-infrared J and K[subscript s] images of the Andromeda Galaxy (M31) taken with WIRCam at the Canada-France-Hawaii Telescope as part of the Andromeda Optical and Infrared Disk Survey. This data set allows simultaneous observations of resolved stars and near-infrared (NIR) surface brightness across M31's entire bulge and disk (within R = 22 kpc), permitting a direct test of the stellar composition of near-infrared light in a nearby galaxy. Here we develop NIR observation and reduction methods to recover a uniform surface brightness map across the 3° × 1° disk of M31 with 27 WIRCam fields. Two sky-target nodding strategies are tested, and we find that strictly minimizing sky sampling latency cannot improve background subtraction accuracy to better than 2% of the background level due to spatio-temporal variations in the NIR skyglow. We fully describe our WIRCam reduction pipeline and advocate using flats built from night-sky images over a single night, rather than dome flats that do not capture the WIRCam illumination field. Contamination from scattered light and thermal background in sky flats has a negligible effect on the surface brightness shape compared to the stochastic differences in background shape between sky and galaxy disk fields, which are ~0.3% of the background level. The most dramatic calibration step is the introduction of scalar sky offsets to each image that optimizes surface brightness continuity. Sky offsets reduce the mean surface brightness difference between observation blocks from 1% to <0.1% of the background level, though the absolute background level remains statistically uncertain to 0.15% of the background level. We present our WIRCam reduction pipeline and performance analysis to give specific recommendations for the improvement of NIR wide-field imaging methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".