Image Subtraction Reduction of Open Clusters M35 & NGC 2158 in the <i>K2</i> Campaign 0 Super Stamp
Bibliographic record
Abstract
We observed the open clusters M35 and NGC 2158 during the initial K2 campaign (C0). Reducing these data to high-precision photometric timeseries is challenging due to the wide point-spread function (PSF) and the blending of stellar light in such dense regions. We developed an image-subtraction-based K2 reduction pipeline that is applicable to both crowded and sparse stellar fields. We applied our pipeline to the data-rich C0 K2 super stamp, containing the two open clusters, as well as to the neighboring postage stamps. In this paper, we present our image subtraction reduction pipeline and demonstrate that this technique achieves ultra-high photometric precision for sources in the C0 super stamp. We extract the raw light curves of 3960 stars taken from the UCAC4 and EPIC catalogs and de-trend them for systematic effects. We compare our photometric results with the prior reductions published in the literature. For de-trended TFA-corrected sources in the 12–12.25 magnitude range, we achieve a best 6.5-hour window running rms of 35 ppm, falling to 100 ppm for fainter stars in the 14–14.25 magnitude range. For stars with , our de-trended and 6.5-hour binned light curves achieve the highest photometric precision. Moreover, all our TFA-corrected sources have higher precision on all timescales investigated. This work represents the first published image subtraction analysis of a K2 super stamp. This method will be particularly useful for analyzing the Galactic bulge observations carried out during K2 campaign 9. The raw light curves and the final results of our de-trending processes are publicly available at http://k2.hatsurveys.org/archive/ .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".