Infrared Photometric Analysis of White Dwarfs from the Two Micron All Sky Survey and the<i>Spitzer Space Telescope</i>
Bibliographic record
Abstract
We review the available near- and mid-infrared photometry for white dwarfs obtained from the Two Micron All Sky Survey (2MASS) and by the Spitzer Space Telescope . Both data sets have recently been used to seek white dwarfs with infrared excesses due to the presence of unresolved companions or circumstellar disks, and also to derive the atmospheric parameters of cool white dwarfs. We first attempt to evaluate the reliability of the 2MASS photometry by comparing it with an independent set of published JHK CIT magnitudes for 160 cool white dwarf stars, and also by comparing the data with the predictions of detailed model atmosphere calculations. The possibility of using 2MASS to identify unresolved M dwarf companions or circumstellar disks is then discussed. We also revisit the analysis of 46 binary candidates from Wachter et al. using the synthetic flux method and confirm the large near-infrared excesses in most objects. We perform a similar analysis by fitting Spitzer 4.5 and 8 μm photometric observations of white dwarfs with our grid of model atmospheres, and demonstrate the reliability of both the Spitzer data and the theoretical calculations up to 8 μm. Finally, we search for massive disks resulting from the merger of two white dwarfs in a 2MASS sample composed of 57 massive degenerates, and show that massive disks are uncommon in such stars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".