Near-infrared photometry of the central stars of planetary nebulae with the VVVX survey
Bibliographic record
Abstract
Achieving accurate photometric characterizations of central stars of planetary nebulae (CSPNe) toward the galactic plane is significantly hindered by the high levels of interstellar extinction in these regions. However, near-infrared (NIR) observations offer a more effective alternative, as extinction is substantially reduced at these wavelengths. By mitigating the effects of interstellar extinction via NIR observations of the Galactic disk and bulge, we seek to improve the identification and characterization of CSPNe in these regions, enabling a deeper understanding of their properties and evolutionary status. We used NIR photometry from the VISTA Variables in the Vía Láctea (VVV) survey and its extension VVVX to define the NIR photometry of a large sample of CSPNe recently identified with Gaia EDR3 data. We explored the optical and NIR properties of all CSPNe in our sample and searched for eclipsing binary candidates among them by employing relevant catalogs. We present a homogeneous catalog of 1274 CSPNe, including their Z, Y, H, J, and K NIR magnitudes and errors. We also include our findings on the photometric properties of our sample. We report 14 CSPNe with a large IR excess indicating cool companions and/or surrounding discs and 56 eclipsing binary candidates. Based on the present VVVX CSPN catalog, we conclude that NIR photometry can prove valuable for further and in-depth studies of CSPNe. Subsequent studies ought to focus on exploring the true nature of the CSPNe that present IR excess as well as eclipsing-binary CSPNe candidates.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 0.000 |
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".